Computer Lab Quotes

Hello, King Morgan,” said Gabriel, popping his head into the lab. “And how is the planet’s only non-idiot on this fine day?” “Screw you,” replied Morgan, without turning from his computer. “Ah, excellent,” said Gabriel. “I’m having a lovely morning, too.

Faith does not protect you. Medicine and airbags... Those are the things that protect you. God does not protect you. Intelligence protects you. Enlightenment. Put your faith in something with tangible results. How long has it been since someone walked on water? Modern miracles belong to science.. Computers, vaccines, space stations... Even the devine miracle of creation. Matter from nothing... In a lab. Who needs God? No! Science is God!

What are you two doing in here?” Mike Iglehart wore an eye-blistering white lab coat and a surly expression. “This isn’t some teenage make-out room.” My face flushed scarlet. “Excuse me?” “We were using the computer!” Ben barked. “That’s it.

The Matrix has its roots in primitive arcade games,' said the voice-over, 'in early graphics programs and military experimentation with cranial jacks.' On the Sony, a two-dimensional space war faded behind a forest of mathematically generated ferns, demonstrating the spatial possibilities of logarithmic spirals; cold blue military footage burned through, lab animals wired into test systems, helmets feeding into fire control circuits of tanks and war planes. 'Cyberspace. A consensual hallucination experienced daily by billions of legitimate operators, in every nation, by children being taught mathematical concepts... A graphic representation of data abstracted from the banks of every computer in the human system. Unthinkable complexity. Lines of light ranged in the nonspace of the mind, clusters and constellations of data. Like city lights, receding...

I got a demerit, professor." There was a kind of naughty amusement in her eyes that I found myself really liking. I smiled slowly. "Why did you do, Miss Dearly?" "She henpecked Elpinoy in a most spectacular fashion," Renfield offered. "I think at one point she was actually hanging on his back." Nora made a sound of annoyance. "Alas, I was looking at a computer screen with Dr. Samedi at the time, and thus I'm afraid that neither of us can vouch for this with certainty." The laughter bubbled out of me before I could hold it back. "Were you?" I asked her. "Define 'hanging.'" "Bra,." Elpinoy appeared in one of the lab doorways. He gestured to the exterior doors. "Take her out. Now. Never in my life have I encountered such a little-" "Lady?" I asked, trying to keep a straight face. "Out." "'Phone call,'" Nora said, affecting his tone of voice and looking right at him. "'Let-ter.'" "Not until Wolfe orders it!" Elpinoy marched into his lab again and slammed the door behind him. Nora stood up, her skirt bouncing a bit atop its puffy petticoat. "That man is an infuriating ponce." "And you're an excellent judge of character.

Bliss, wait one second.” I took my time packing up my stuff, waiting for everyone else to leave the computer lab. When we were alone I asked, “What’s up?” He smiled, “Nothing.” Then he pressed me into the computer table behind me and kissed me.

You correctly predicted the rise of heroin while Bush was in office,” he said. “And people still don’t think to ask why his Yale Skull and Bones fraternity name is ‘Poppy.’ Since Clinton is more heavily involved in cocaine ops than he is Bush’s heroin ops7, the price of coke and crack will probably drop in this country while availability soars. ” Mark agreed. “The Presidency switched parties all right, from a heroin party to a coke party with all the same players involved.” “Except for the kids Bush used and abused,” I said. “Neither Hillary nor Bill believe in pedophilia. From my point of view, that is a major difference between the Bushes and Clintons. Other than that, they’re playing the same DARPA-Sandia Labs computer game.

In 1948, while working for Bell Telephone Laboratories, he published a paper in the Bell System Technical Journal entitled "A Mathematical Theory of Communication" that not only introduced the word bit in print but established a field of study today known as information theory. Information theory is concerned with transmitting digital information in the presence of noise (which usually prevents all the information from getting through) and how to compensate for that. In 1949, he wrote the first article about programming a computer to play chess, and in 1952 he designed a mechanical mouse controlled by relays that could learn its way around a maze. Shannon was also well known at Bell Labs for riding a unicycle and juggling simultaneously.

Want to guess what comes up when I Google “Woman discovers”? It’s not “new galaxy.” It’s “a body in her trunk” or "the unthinkable in her attic.” According to my computer search, other big discoveries by women include “her co-worker is her birth mom,” “a Renaissance painting in her kitchen,” and “her new home was once a meth lab.” Hey, at least that one contains the word “lab.

It came from the world’s first computer – the Mark 1 – a room-size maze of electromechanical circuits built in 1944 in a lab at Harvard University. The computer developed a glitch one day, and no one was able to locate the cause. After hours of searching, a lab assistant finally spotted the problem. It seemed a moth had landed on one of the computer’s circuit boards and shorted it out. From that moment on, computer glitches were referred to as bugs.

One of the first people I interviewed was Alvy Ray Smith, a charismatic Texan with a Ph.D. in computer science and a sparkling resume that included teaching stints at New York University and UC Berkeley and a gig at Xerox PARC, the distinguished R&D lab in Palo Alto. I had conflicting feelings when I met Alvy because, frankly, he seemed more qualified to lead the lab than I was. I can still remember the uneasiness in my gut, that instinctual twinge spurred by a potential threat: This, I thought, could be the guy who takes my job one day. I hired him anyway.

Tower and get up to the top floor. There’s a room up there with a computer in it where you can turn off the incinerator. There’s another computer that will override the lockdown system. It’s pretty simple. The hard part is getting in there. My card will get you into The Alpha Tower, but once you’re in, you’ll need a scientist’s card to get to the last room. As far as I know, they’ve all been bitten. It’s a tower full of diseased now, but if you can kill one in a lab coat, you may find a card. I think it’s suicide though, Rhys.” When Rhys looked at Flynn, the light glistened off his tear-streaked cheeks. “Can

For example, in 2012 researchers at Kaspersky Lab in Moscow uncovered a highly complex piece of malware known as Flame that had been pilfering data from information systems around the world for more than five years before it was detected. Mikko Hypponen, the well-respected chief research officer at the computer security firm F-Secure, called Flame a failure for the antivirus industry and noted he and his colleagues may be “out of their leagues in their own game.” Though millions around the world rely on these tools, it’s pretty clear the antivirus era is over.

Well, at least you’ll have Bob the Copying Machine to keep you warm at night.” “Um. His name is Franklin,” Monty said, holding up a hand. “Get it right.” Seeing a blinking light on the machine, I pointed to it. “Looks like Franklin is out of paper. You should fix that.” “Oh, I’ll stuff him real good,” Monty said, before grabbing paper from the pack and slowly sticking it into the slot. “Damn, he’s all nice and tight now.” I cleared my throat. Sherry, the computer lab teacher, had walked into the lounge. Monty flipped around, the smile vanishing instantly as he spotted her. “I, uh, was just refilling the paper,” he said, pointing to the machine. “Good job,” Sherry said, looking at him like he belonged in a mental ward.

I feel like finally, I’ve gotten it together: I’ve hit my stride. I can do this. So when I walk into school that cold January morning, holding Peter’s hand, full on banana pancakes, with a new job and wearing Margot’s Fair Isle sweater she left behind, I am feeling good. Great, even. Peter wants to stop in the computer lab to print out his English paper, so that’s our first stop. He logs in, and I gasp out loud when I see the wallpaper. Someone has taken a still of the hot tub video, of me in Peter’s lap in my red flannel nightgown, skirt hitched up around my thighs, and across the top it reads HOT HOT TUB SEX. And on the bottom--YOU’RE DOING IT WRONG. “What the hell?” Peter mutters, looking around the computer lab. Nobody looks up. He goes to the next computer--same picture, different caption. SHE DOESN’T KNOW ABOUT SHRINKAGE on top. HE’S HAPPY WITH WHAT HE CAN GET across the bottom. We are a meme.

Missy and her crew left, I was alone. Like really alone, like pre-Shay alone. It felt glorious. Well, maybe not. I didn’t feel right about Shay, but I’d see him in a day. We could sort out whatever happened on his street. Till then, I studied to my heart’s content. I made trips to my dorm’s computer lab, and I even got naughty. I stole some of the computer’s printing papers, stuffing them down the front of my shirt. My inner dork was coming out full-force. It was like I’d been around “cool” people too much for my system. It was rebelling. It needed an outlet, and I indulged. All of the colored highlighters came out. Not just the primary colors, all of them. I used pink for one textbook, and added purple on the next. All caution was thrown to the wind. It was only eight, but I went to the library. I really let my freak out. An energy drink. Coffee from the cart. My own Twizzlers this time. Even a bag of chocolate candies. I was going nuts on the caffeine and sugar, and then I found an empty study room on the top and most isolated floor in the library. I stayed until midnight. It was some of the best studying I’ve had. Ever. Mind-blowing.

It was little things at first. Abby missed a phone call because she had an away game. Then one time Gretchen didn’t write back and never made up for the missing letter. They got busy with SATs and college applications, and even though they both applied to Georgetown, Gretchen didn’t get in, and Abby wound up going to George Washington anyways. At college they went to their computer labs and sent each other emails, sitting in front of black and green CRT screens and pecking them out one letter at a time. And they still wrote, but calling became a once-a-week thing. Gretchen was Abby’s maid of honor at her tiny courthouse wedding, but sometimes a month would go by and they wouldn’t speak. Then two months. Then three. They went through periods when they both made an effort to write more, but after a while that usually faded. It wasn’t anything serious, it was just life. The dance recitals, making the rent, first real jobs, pickups, dropoffs, the fights that seemed so important, the laundry, the promotions, the vacations taken, shoes bought, movies watched, lunches packed. It was a haze of the everyday that blurred the big things and made them feel distant and small.

At a law school in Canada, we are in deep discussion of the law as a universal instrument that feminists should expect to be flexible. I am arguing that this is what judges are for - otherwise, justice could be meted out by a computer. The mostly male law students are arguing that any exception is dangerous and creates a "slippery slope." Make one exception, and the number will grow until the law will be overturned de facto. I am not a lawyer. I am stuck. Those young men may or may not represent the common-sense majority in the audience, but they have triumphed. Then a tall young woman in jeans rises from the back of the room. "Well," she says calmly, "I have a boa constrictor." This quiets the audience right down. "Once a month," she continues, "I go to the dissection lab on campus to get frozen mice to feed my boa constrictor. But this month, there is a new professor in charge, and he said to me 'I can't give you frozen mice. If I give you frozen mice, everyone will want frozen mice." There is such an explosion of laughter that even the argumentative young men can't resist. She has made her point: not everyone wants the same thing. A just law can be flexible. To be just, a law has to be flexible. She has saved the day.

Eyebrows were raised in 1994 when Peter Shor, working at Bell Labs, came up with a quantum algorithm that could break most modern encryption by using quantum computing algorithms. Today’s encryption is based on the difficulty of factoring large numbers. Even today, although there are no quantum computers that can implement Shor’s algorithm in full yet, there is worry that most of our encryption will be broken in a few years as more capable quantum computers come along. When this happens, there will be a rush to quantum-safe encryption algorithms (which cannot be broken quickly by either classic or quantum computers).

That’s why traditional religions offer no real alternative to liberalism. Their scriptures don’t have anything to say about genetic engineering or artificial intelligence, and most priests, rabbis and muftis don’t understand the latest breakthroughs in biology and computer science. For if you want to understand these breakthroughs, you don’t have much choice – you need to spend time reading scientific articles and conducting lab experiments instead of memorising and debating ancient texts. That doesn’t mean liberalism can rest on its laurels. True, it has won the humanist wars of religion, and as of 2016 it has no viable alternative. But its very success may contain the seeds of its ruin. The triumphant liberal ideals are now pushing humankind to reach for immortality, bliss and divinity. Egged on by the allegedly infallible wishes of customers and voters, scientists and engineers devote more and more energies to these liberal projects. Yet what the scientists are discovering and what the engineers are developing may unwittingly expose both the inherent flaws in the liberal world view and the blindness of customers and voters. When genetic engineering and artificial intelligence reveal their full potential, liberalism, democracy and free markets might become as obsolete as flint knives, tape cassettes, Islam and communism.

Charlotte had the blondest hair I’ve ever seen. She didn’t shake my hand but gave me a quick little wave and smiled. “Hi, August. Nice to meet you,” she said. “Hi,” I said, looking down. She was wearing bright green Crocs. “So,” said Mr. Tushman, putting his hands together in a kind of slow clap. “What I thought you guys could do is take August on a little tour of the school. Maybe you could start on the third floor? That’s where your homeroom class is going to be: room 301. I think. Mrs. G, is—” “Room 301!” Mrs. Garcia called out from the other room. “Room 301.” Mr. Tushman nodded. “And then you can show August the science labs and the computer room. Then work your way down to the library and the performance space on the second floor. Take him to the cafeteria, of course.” “Should we take him to the music room?” asked Julian. “Good idea, yes,” said Mr. Tushman. “August, do you play any instruments?” “No,” I said. It wasn’t my favorite subject on account of the fact that I don’t really have ears. Well, I do, but they don’t exactly look like normal ears. “Well, you may enjoy seeing the music room anyway,” said Mr. Tushman. “We have a very nice selection of percussion instruments.” “August, you’ve been wanting to learn to play the drums,” Mom said, trying to get me to look at her. But my eyes were covered by my bangs as I stared at a piece of old gum that was stuck to the

Evan was attracted to technology early on, building his first computer in sixth grade and experimenting with Photoshop in the Crossroads computer lab. He would later describe the computer teacher, Dan, as his best friend. Evan dove into journalism as well, writing for the school newspaper, Crossfire. One journalism class required students to sell a certain amount of advertising for Crossfire as part of their grade. Evan walked around the neighborhood asking local businesses to buy ads; once he had exceeded his sales goals, he helped coach his peers on how to pitch businesses and ask adults for money. By high school, the group of 20 students Evan had started with in kindergarten had grown to around 120. Charming, charismatic, and smart, Evan threw parties at his dad’s house that were “notorious” in his words. Evan’s outsized personality could rub people the wrong way at times, but his energy, organizing skills, and enthusiasm made him an exceptional party thrower. He possessed a bravado that could be frustrating and off-putting but was great for convincing everyone that the night’s party was going to be the greatest of all time. Obsessed with the energy drink Red Bull and the lifestyle the brand cultivated, Evan talked his way into an internship at the company as a senior in high school. The job involved throwing parties and other events sponsored by Red Bull. Clarence Carter, the head of the company’s security team, would give Evan advice that would stand him well in the years to come: pay attention to who helps you clean up after the party. Later recalling the story, Evan said, “When everyone is tired and the night is over, who stays and helps out? Because those are your true friends. Those are the hard workers, the people that believe that working hard is the right thing to do.

As World War II was ending, the great engineer and public official Vannevar Bush argued that America’s innovation engine would require a three-way partnership of government, business, and academia. He was uniquely qualified to envision that triangle, because he had a foot in all three camps. He had been dean of engineering at MIT, a founder of Raytheon, and the chief government science administrator overseeing, among other projects, the building of the atom bomb.4 Bush’s recommendation was that government should not build big research labs of its own, as it had done with the atomic bomb project, but instead should fund research at universities and corporate labs. This government-business-university partnership produced the great innovations that propelled the U.S. economy in the postwar period, including transistors, microchips, computers, graphical user interfaces, GPS, lasers, the internet, and search engines.

THE SK8 MAKER VS. GLOBAL INDUSTRIALIZATION This new era of global industrialization is where my personal analogy with the history of the skateboard maker diverges. It’s no longer cost-effective to run a small skateboard company in the U.S., and the handful of startups that pull it off are few and far between. The mega manufacturers who can churn out millions of decks at low cost and record speed each year in Chinese factories employ proprietary equipment and techniques that you and I can barely imagine. Drills that can cut all eight truck holes in a stack of skateboard decks in a single pull. CNC machinery to create CAD-perfect molds used by giant two-sided hydraulic presses that can press dozens of boards in a few hours. Computer-operated cutting bits that can stamp out a deck to within 1⁄64 in. of its specified shape. And industrial grade machines that apply multicolored heat-transfer graphics in minutes. In a way, this factory automation has propelled skateboarding to become a multinational, multi-billion dollar industry. The best skateboarders require this level of precision in each deck. Otherwise, they could end up on their tails after a failed trick. Or much worse. As the commercial deck relies more and more on a process that is out of reach for mere mortals, there is great value in the handmade and one of a kind. Making things from scratch is a dying art on the brink of extinction. It was pushed to the edge when public schools dismissed woodworking classes and turned the school woodshop into a computer lab. And when you separate society from how things are made—even a skateboard—you lose touch with the labor and the materials and processes that contributed to its existence in the first place. It’s not long before you take for granted the value of an object. The result is a world where cheap labor produces cheap goods consumed by careless customers who don’t even value the things they own.

The school is teeming with activity. The rooms are small and large, many are special-purpose rooms, like shops and labs, but most are furnished like rather shabby living or dining rooms in homes: lots of sofas, easy chairs, and tables. Lots of people sitting around talking, reading, and playing games. On an average rainy day—quite different from a beautiful suddenly snowy day, or a warm spring or fall day—most people are inside. But there will also be more than a few who are outside in the rain, and later will come in dripping and trying the patience of the few people inside who think the school should perhaps be a “dry zone.” There may be people in the photo lab developing or printing pictures they have taken. There may be a karate class, or just some people playing on mats in the dance room. Someone may be building a bookshelf or fashioning chain mail armor and discussing medieval history. There are almost certainly a few people, either together or separate, making music of one kind or another, and others listening to music of one kind or another. You will find adults in groups that include kids, or maybe just talking with one student. It would be most unusual if there were not people playing a computer game somewhere, or chess; a few people doing some of the school’s administrative work in the office—while others hang around just enjoying the atmosphere of an office where interesting people are always making things happen; there will be people engaged in role-playing games; other people may be rehearsing a play—it might be original, it might be a classic. They may intend production or just momentary amusement. People will be trading stickers and trading lunches. There will probably be people selling things. If you are lucky, someone will be selling cookies they baked at home and brought in to earn money. Sometimes groups of kids have cooked something to sell to raise money for an activity—perhaps they need to buy a new kiln, or want to go on a trip. An intense conversation will probably be in progress in the smoking area, and others in other places. A group in the kitchen may be cooking—maybe pizza or apple pie. Always, either in the art room or in any one of many other places, people will be drawing. In the art room they might also be sewing, or painting, and some are quite likely to be working with clay, either on the wheel or by hand. Always there are groups talking, and always there are people quietly reading here and there. One

During homeroom, before first period, I start a bucket list in one of my notebooks. First on the list? 1) Eat in the cafeteria. Sit with people. TALK TO THEM. 2) And…that’s all I can come up with for now. But this is good. One task to work on. No distractions. I can do this. When my lunch period rolls around, I forgo the safety of my bag lunch and the computer lab and slip into the pizza line, wielding my very own tray of semi-edible fare for the first time in years. “A truly remarkable sight.” Jensen cuts into line beside me, sliding his tray next to mine on the ledge in front of us. He lifts his hands and frames me with his fingers, like he’s shooting a movie. “In search of food, the elusive creature emerges from her den and tries her luck at the watering hole." I shake my head, smiling, moving down the line. “Wow, Peters. I never knew you were such a huge Animal Planet fan.” “I’m a fan of all things nature. Birds. Bees. The like.” He grabs two pudding cups and drops one on my tray. “Pandas?” I say. “How did you know? The panda is my spirit animal.” “Oh, good, because Gran has this great pattern for an embroidered panda cardigan. It would look amazing on you.” “Um, yeah, I know. It was on my Christmas list, but Santa totally stiffed me." I laugh as I grab a carton of milk. So does he. He leans in closer. “Come sit with me.” “At the jock table? Are you kidding?” I hand the cashier my lunch card. Jensen squints his eyes in the direction of his friends. “We’re skinny-ass basketball players, Wayfare. We don’t really scream jock.” “Meatheads, then?” “I believe the correct term is Athletic Types.” We step out from the line and scan the room. “So where were you planning on sitting?" “I was thinking Grady and Marco were my safest bet.” “The nerd table?” I gesture to myself, especially my glasses. “I figure my natural camouflage will help me blend, yo.” He laughs, his honey-blond hair falling in front of his eyes. “And hey,” I say, nudging him with my elbow, “last I heard, Peters was cool with nerdy.” He claps me gently on the back. “Good luck, Wayfare. I’m pulling for ya.

Who is going to fight them off, Randy?” “I’m afraid you’re going to say we are.” “Sometimes it might be other Ares-worshippers, as when Iran and Iraq went to war and no one cared who won. But if Ares-worshippers aren’t going to end up running the whole world, someone needs to do violence to them. This isn’t very nice, but it’s a fact: civilization requires an Aegis. And the only way to fight the bastards off in the end is through intelligence. Cunning. Metis.” “Tactical cunning, like Odysseus and the Trojan Horse, or—” “Both that, and technological cunning. From time to time there is a battle that is out-and-out won by a new technology—like longbows at Crecy. For most of history those battles happen only every few centuries—you have the chariot, the compound bow, gunpowder, ironclad ships, and so on. But something happens around, say, the time that the Monitor, which the Northerners believe to be the only ironclad warship on earth, just happens to run into the Merrimack, of which the Southerners believe exactly the same thing, and they pound the hell out of each other for hours and hours. That’s as good a point as any to identify as the moment when a spectacular rise in military technology takes off—it’s the elbow in the exponential curve. Now it takes the world’s essentially conservative military establishments a few decades to really comprehend what has happened, but by the time we’re in the thick of the Second World War, it’s accepted by everyone who doesn’t have his head completely up his ass that the war’s going to be won by whichever side has the best technology. So on the German side alone we’ve got rockets, jet aircraft, nerve gas, wire-guided missiles. And on the Allied side we’ve got three vast efforts that put basically every top-level hacker, nerd, and geek to work: the codebreaking thing, which as you know gave rise to the digital computer; the Manhattan Project, which gave us nuclear weapons; and the Radiation Lab, which gave us the modern electronics industry. Do you know why we won the Second World War, Randy?” “I think you just told me.” “Because we built better stuff than the Germans?” “Isn’t that what you said?” “But why did we build better stuff, Randy?” “I guess I’m not competent to answer, Enoch, I haven’t studied that period well enough.” “Well the short answer is that we won because the Germans worshipped Ares and we worshipped Athena.” “And am I supposed to gather that you, or

With the introduction of radio, we now had a superfast. convenient, and wireless way of communicating over long distances. Historically, the lack of a fast and reliable communication system was one of the great obstacles to the march of history. (In 490 BCE, after the Battle of Marathon between the Greeks and the Persians, a poor runner was ordered to spread the news of the Greek victory as fast as he could. Bravely, he ran 26 miles to Athens after previously running 147 miles to Sparta, and then, according to legend, dropped dead of sheer exhaustion. His heroism, in the age before telecommunication, is now celebrated in the modern marathon.) Today, we take for granted that we can send messages and information effortlessly across the globe, utilizing the fact that energy can be transformed in many ways. For example, when speaking on a cell phone, the energy of the sound of your voice converts to mechanical energy in a vibrating diaphragm. The diaphragm is attached to a magnet that relies on the interchangeability of electricity and magnetism to create an electrical impulse, the kind that can be transported and read by a computer. This electrical impulse is then translated into electromagnetic waves that are picked up by a nearby microwave tower. There, the message is amplified and sent across the globe. But Maxwell's equations not only gave us nearly instantaneous communication via radio, cell phone, and fiber-optic cables, they also opened up the entire electromagnetic spectrum, of which visible light and radio were just two members. In the 166os, Newton had shown that white light, when sent through a prism, can be broken up into the colors of the rainbow. In 1800, William Herschel had asked himself a simple question: What lies beyond the colors of the rainbow, which extend from red to violet? He took a prism, which created a rainbow in his lab, and placed a thermometer below the color red, where there was no color at all. Much to his surprise, the temperature of this blank area began to rise. In other words, there was a "color" below red that was invisible to the naked eye but contained energy. It was called infrared light. Today, we realize that there is an entire spectrum of electromagnetic radiation, most of which is invisible, and each has a distinct wavelength. The wavelength of radio and TV, for example, is longer than that of visible light. The wavelength of the colors of the rainbow, in turn, is longer than that of ultraviolet and X-rays. This also meant that the reality we see all around us is only the tiniest sliver of the complete EM spectrum, the smallest approximation of a much larger universe

In 1958 AT&T, the owner of Bell Labs, was served with an antitrust court order that forbade it to ever enter the computer business and that forced it to license any non-telephone inventions to the whole world. This odd ruling turned Unix into a worldwide phenomenon, as it spread from one corner of the computer world to the other.

Table of Content Chapter 1 - Basic Networking Elements 1) Network Types 2) Network Topologies 3) Network Components A. END DEVICES & MEANS FOR TRANSMISSION B. SWITCH C. ROUTER 4) How can we represent (or “draw”) a network ? 5) How computers communicate over the Internet ? Chapter 2 – Switches, Ethernet and MAC addresses What’s Ethernet ? Chapter 3 – Routers, IPv4 & IPv6 addresses Basic Routing concepts The IPv4 Protocol IPv4 Classes Public IP vs Private IP Configuring an IP address on Windows 7/8/10 The IPv6 Protocol Chapter 4 – TCP, UDP, Ports and Network Applications 1) TCP and UDP 2) Ports 3) Network Applications Chapter 5 - Cisco IOS & Intro to the CLI Introduction to the CLI - Basic Router Configurations LAB #1

In terms of funding, Google dwarfs even its own government: U.S. federal funding for math and computer science research amounts to less than half of Google’s own R&D budget. That spending spree has bought Alphabet an outsized share of the world’s brightest AI minds. Of the top one hundred AI researchers and engineers, around half are already working for Google. The other half are distributed among the remaining Seven Giants, academia, and a handful of smaller startups. Microsoft and Facebook have soaked up substantial portions of this group, with Facebook bringing on superstar researchers like Yann LeCun. Of the Chinese giants, Baidu went into deep-learning research earliest—even trying to acquire Geoffrey Hinton’s startup in 2013 before being outbid by Google—and scored a major coup in 2014 when it recruited Andrew Ng to head up its Silicon Valley AI Lab.

Alec enjoyed that childhood memory. Her committee had raised enough money to stock the school computer lab with new Apple computers. He still remembered the pride he’d felt when other kids were dazzled by the extravagant equipment.

What is the use of religion in the modern world? In my mind, religion can be of no use for any intellectual in the sense what is directly written there. Whatever literally means in religion can independently be thought by the rational mind, of course, if there is any need to think over such mythology. Most of what rational mind can obtain in scientific way cannot even be discernible by religious thinking. There is no need to emphasize that the fundamental base of the modern world is out of the province of religious thinking. The language of religion itself is out of date. The whole description of afterdeath, for example, seems trivial nowadays. Imagine that afterdeath you go to pass the examination of ‘goodness’ or ‘badness’ before the Holy Spirit as it is shown in religion. He asks what you have done in so-and-so time and space, and you answer miraculously remembering every detail in your life, fully visualizing everything before your eyes. Nothing can be forgotten, nothing can be hidden. I don’t claim, whether it is true or false, at least here and now, I want to emphasize the fact that the language of such description is out of date. Considering that the history of religion and the Holy Scripture goes back long, long before the emergence of the modern world, it is explainable. Whatever is written there coincides with pre-modern world thinking. For instance, if a religion emerged nowadays, its Holy Scripture would contain concepts and ideas according to modern world thinking. There would be no need for questioning you by the Holy Spirit. In my eyes, there would be rather a description of the computer lab with ‘angels’ sitting at the computer and checking your memory-card in which is written all information about your life. The brain, which can pass the examination of ‘goodness’ would be connected to ‘heaven’ program system, which in turn would send to that brain positive signal as if your body is really experiencing sexual or other kind of pleasures as it is described in religion. Similarly, if you brain fails in that examination, it would be connected to ‘hell’ program system sending to your brain negative signals as if your body is really undergoing the punishment known by religion. So can modern religion be of the modern age. The human mind can never entirely grasp what would happen after death, so why should you poison your mind with mythology from ages past?

VR is not as new as it seems: It germinated inside Alan Kay’s research lab at Atari. The lab collapsed when Atari did, scattering Kay’s people across the Valley, but a young, dreadlocked, programming prodigy, Jaron Lanier, continued the research on his own dime. His original goal was to revive an old dream. Like Doug Engelbart and Alan Kay, Lanier wanted to create a computing environment that was immersive, flexible, and empowering. The difference was the interface. Engelbart invented the mouse. Alan Kay added the desktop metaphor. And in Lanier’s iteration, one donned goggles and gloves and stepped into virtual reality. Lanier actually coined the phrase. And the whole point of this new, all-enveloping interface was to be able to program the computer from the inside. There was just one problem: Once people got inside the computer, virtually no one wanted to code. There was a whole world in there, a cyberdelic Disneyland just waiting to be explored. Lanier thought he was building a next-generation programming language with the corresponding next-generation graphical user interface, but what people experienced was something a lot more fun. VR was The Well’s cyberspace made real. Taking advantage of the ensuing limelight, Lanier swiftly assumed a more Jobs-like role and marketed the heck out of his virtual reality machine, but in the end, the cost of an E ticket was just too high.

Bruce Horn: I thought that computers would be hugely flexible and we could be able to do everything and it would be the most mind-blowing experience ever. And instead we froze all of our thinking. We froze all the software and made it kind of industrial and mass-marketed. Computing went in the wrong direction: Computing went to the direction of commercialism and cookie-cutter. Jaron Lanier: My whole field has created shit. And it’s like we’ve thrust all of humanity into this endless life of tedium, and it’s not how it was supposed to be. The way we’ve designed the tools requires that people comply totally with an infinite number of arbitrary actions. We really have turned humanity into lab rats that are trained to run mazes. I really think on just the most fundamental level we are approaching digital technology in the wrong way. Andy van Dam: Ask yourself, what have we got today? We’ve got Microsoft Word and we’ve got PowerPoint and we’ve got Illustrator and we’ve got Photoshop. There’s more functionality and, for my taste, an easier-to-understand user interface than what we had before. But they don’t work together. They don’t play nice together. And most of the time, what you’ve got is an import/export capability, based on bitmaps: the lowest common denominator—dead bits, in effect. What I’m still looking for is a reintegration of these various components so that we can go back to the future and have that broad vision at our fingertips. I don’t see how we are going to get there, frankly. Live bits—where everything interoperates—we’ve lost that. Bruce Horn: We’re waiting for the right thing to happen to have the same type of mind-blowing experience that we were able to show the Apple people at PARC. There’s some work being done, but it’s very tough. And, yeah, I feel somewhat responsible. On the other hand, if somebody like Alan Kay couldn’t make it happen, how can I make it happen?

In 1967, Zuse suggested that the universe itself was running on a cellu- lar automaton or a similar computational structure, a metaphysical position known today as digital physics, a subject Ed Fredkin had himself taken up before becoming acquainted with the work of Zuse. Excited to discover this work, Fredkin invited Zuse to Cambridge, MA. The translation of Rechnen- der Raum reproduced here, from a German (published) version of Zuse’s ideas, was in fact commissioned during Ed Fredkin’s tenure as Director of MIT’s Project MAC10 (the AI lab that was a precursor of the current MIT AI labs)

What in the—? My begonias!” he heard someone say behind him. Nick looked over his shoulder. A small but muscular woman in sweaty workout clothes was stepping out of a big shiny car in the neighbor’s driveway. She was gaping in horror at the chewed-up flowerbed and the smoking lawn mower. Scowling, she turned toward Uncle Newt’s house. And the scowl didn’t go away when she noticed Nick looking back at her. In fact, it got scowlier. Nick smiled weakly, waved, and hurried into the house. He closed the door behind him. “Whoa,” he said when his eyes adjusted to the gloom inside. Cluttering the long hall in front of him were dozens of old computers, a telescope, a metal detector connected to a pair of bulky earphones, an old-fashioned diving suit complete with brass helmet, a stuffed polar bear (the real, dead kind), a chainsaw, something that looked like a flamethrower (but couldn’t be … right?), a box marked KEEP REFRIGERATED, another marked THIS END UP (upside down), and a fully lit Christmas tree decorated with ornaments made from broken beakers and test tubes (it was June). Exposed wires and power cables poked out of the plaster and veered off around every corner, and there were so many diplomas and science prizes and patents hanging (all of them earned by Newton Galileo Holt, a.k.a. Uncle Newt) that barely an inch of wall was left uncovered. Off to the left was a living room lined with enough books to put some libraries to shame, a semitransparent couch made of inflated plastic bags, and a wide-screen TV connected by frayed cords to a small trampoline.

Inside Hod Lipson’s Creative Machines Lab at Cornell University, fantastically shaped robots are learning to crawl and fly, probably even as you read this. One looks like a slithering tower of rubber bricks, another like a helicopter with dragonfly wings, yet another like a shape-shifting Tinkertoy. These robots were not designed by any human engineer but created by evolution, the same process that gave rise to the diversity of life on Earth. Although the robots initially evolve inside a computer simulation, once they look proficient enough to make it in the real world, solid versions are automatically fabricated by 3-D printing. These are not yet ready to take over the world, but they’ve come a long way from the primordial soup of simulated parts they started with. The algorithm that evolved these robots was invented by Charles Darwin in the nineteenth century. He didn’t think of it as an algorithm at the time, partly because a key subroutine was still missing. Once James Watson and Francis Crick provided it in 1953, the stage was set for the second coming of evolution: in silico instead of in vivo, and a billion times faster. Its prophet was a ruddy-faced, perpetually grinning midwesterner by the name of John Holland.

Radios, vacuum tubes, transistors, televisions, solar cells, coaxial cables, laser beams, microprocessors, computers, cell phones, fiber optics—all these essential tools of modern life descend from ideas originally generated at Bell Labs.

Microsoft azure course focuses to cover a scope of parts, including Azure Compute, Azure Storage, and system benefits that clients can profit by while sending half breed arrangements. The preparation gives the learning to the members which thusly is basic to plan Hybrid arrangements appropriately. It likewise incorporates various showings and labs that empower understudies to create hands-on abilities that are essential for fruitful usage of such arrangements. Implementing microsoft azure infrastructure solutions.

He contrasted what he called the MIT style, where correctness trumps everything else, and the New Jersey (i.e. Bell Labs) style, where simplicity of implementation is highly valued. His theory was that the New Jersey style, which he also called “Worse Is Better” made it possible to get stuff out and running and from there it will get improved.

Back at his apartment after ten hours in the computer lab, Adam turns on the TV. Oil wars and sectarian violence. It’s way too early to think about sleeping, though that’s all he wants to do. He’s still a score of stories up in the air, held aloft by a nonexistent tree, listening to the creak of that high house and the calls of birds he’d like to be able to name. He tries to read a novel, something about privileged people having trouble getting along with each other in exotic locations. He throws it against the wall. Something has broken in him. His appetite for human self-regard is dead.

Kelly could perceive the obvious differences between IBM and Bell Labs. IBM was a computer company, first and foremost, and not a communications company. “We were moving faster than Bell Labs would,” Gunther-Mohr says, noting that Bell Labs had a thirty-year schedule for applying its inventions to the phone network.

would help rather than hurt, this was it. “How much is it going to cost?” asked the woman at Zuckerberg General, after the team at Chan Zuckerberg had explained their new COVID-19 testing lab. “It’s free,” said the Chan Zuckerberg person. “There was this super-long pause,” said Joe, who was on the line. “We don’t know how to do no-cost,” said Zuckerberg. “What do you mean?” asked Chan Zuckerberg. “It shows up as an error in the hospital computer if we put zero cost,” said Zuckerberg. “It won’t accept zero.

As word spread, Shannon’s slender and highly mathematical paper, about twenty-five pages in all, would ultimately become known as the most influential master’s thesis in history.9 In time, it would influence the design of computers that were just coming into existence as well as those that wouldn’t be built for at least another generation.

We now use our phones so habitually that I don’t think we consider doing a task and checking our phones at the same time as multitasking, any more than we think scratching your butt during a work call is multitasking. But it is. Simply having your phone switched on and receiving texts every ten minutes while you try to work is itself a form of switching – and these costs start to kick in for you too. One study at the Carnegie Mellon University’s Human Computer Interaction Lab took 136 students and got them to sit a test. Some of them had to have their phones switched off, and others had their phones on and received intermittent text messages. The students who received messages performed, on average, 20 percent worse.

The out-of-the-box California physicists beat their heads against this problem for years, but by the early 1980s, it became apparent that there is no way to send a signal via entanglement alone. For one thing, if you force one of a pair of entangled particles into a certain state, the entanglement with the other particle will be broken, so it will not “send” information about its state to its twin. You are limited to performing measurements of a particle’s uncertain value, which compels it to make up its mind about the (previously uncertain) state it is in. In that case, you can be sure its entangled twin will make the same choice, but then some additional information channel needs to be available to let your distant partner know what measurement you performed and what result you got. The latter part of the problem has an analogy in basic semantics. For a piece of information to be meaningful, it needs to be reliably paired with another piece of information that gives it context or serves as its cipher. If I say “yes” to my wife, it can only be meaningless noise, a random word, unless my utterance was produced in the context of a question, like “Are you going to the store later?” Without knowing exactly how the physicist on Earth measured her particle, Alice, and what result she got, the change in Alice’s entangled partner Bob four light years away in that lab orbiting Alpha Centauri cannot be meaningful, even if it is information. The Earth physicist needs to send some slower-than-light signal to inform her distant colleague about her measurement and its outcome … which defeats the whole purpose of using entanglement to carry a message.47 This is also the problem with the metaphor of the universe as a computer. No matter how much computation the universe can perform, its outputs can be little more than out-of-context yesses and nos, addressed to no one in particular. If there is no “outside” to the system, there is nothing to compare it to and no one to give all those bit flips meaning. In fact, it is a lot like the planetary supercomputer “Deep Thought” in Douglas Adam’s Hitchhiker’s Guide to the Galaxy: When, after millions of years of computation, it finally utters its output, “42,” no one knows what it means, because the question the computer had been programmed to answer has long been forgotten. We are now perhaps in a better position to understand how the behavior of atoms, photons, and subatomic particles could carry information about their future—tons of information—without any of it being meaningful to us, and why we would naturally (mis)construe it as randomness: It is noise to our ears, stuck as we are in the Now with no way of interpreting it. It is like the future constantly sending back strings of yesses and nos without us knowing the questions.

The out-of-the-box California physicists beat their heads against this problem for years, but by the early 1980s, it became apparent that there is no way to send a signal via entanglement alone. For one thing, if you force one of a pair of entangled particles into a certain state, the entanglement with the other particle will be broken, so it will not “send” information about its state to its twin. You are limited to performing measurements of a particle’s uncertain value, which compels it to make up its mind about the (previously uncertain) state it is in. In that case, you can be sure its entangled twin will make the same choice, but then some additional information channel needs to be available to let your distant partner know what measurement you performed and what result you got. The latter part of the problem has an analogy in basic semantics. For a piece of information to be meaningful, it needs to be reliably paired with another piece of information that gives it context or serves as its cipher. If I say “yes” to my wife, it can only be meaningless noise, a random word, unless my utterance was produced in the context of a question, like “Are you going to the store later?” Without knowing exactly how the physicist on Earth measured her particle, Alice, and what result she got, the change in Alice’s entangled partner Bob four light years away in that lab orbiting Alpha Centauri cannot be meaningful, even if it is information. The Earth physicist needs to send some slower-than-light signal to inform her distant colleague about her measurement and its outcome … which defeats the whole purpose of using entanglement to carry a message.47 This is also the problem with the metaphor of the universe as a computer. No matter how much computation the universe can perform, its outputs can be little more than out-of-context yesses and nos, addressed to no one in particular. If there is no “outside” to the system, there is nothing to compare it to and no one to give all those bit flips meaning. In fact, it is a lot like the planetary supercomputer “Deep Thought” in Douglas Adam’s Hitchhiker’s Guide to the Galaxy: When, after millions of years of computation, it finally utters its output, “42,” no one knows what it means, because the question the computer had been programmed to answer has long been forgotten. We are now perhaps in a better position to understand how the behavior of atoms, photons, and subatomic particles could carry information about their future—tons of information—without any of it being meaningful to us, and why we would naturally (mis)construe it as randomness: It is noise to our ears, stuck as we are in the Now with no way of interpreting it. It is like the future constantly sending back strings of yesses and nos without us knowing the questions. We are only now realizing that there may indeed be words in all that noise—it’s not just gibberish. But how to decode them?

The MIT Media Lab lists our device as the first of what would later be called wearable computers, namely, computers that are worn on the body as part of their function. In late 1961 I built the second wearable computer, a knockoff to predict the wheel of fortune or money wheel. As in the roulette computer, my device used the toe-operated switch for input, the speaker for output, and just a single unijunction transistor; it required only one person. Matchbox-sized, it worked well in the casinos, but the game had too little action to conceal the spectacular consequences of my late bets. Several times when I placed bets on 40:1 as the wheel was spinning, the croupier would give the wheel an extra push.

The machine was called Superpaint. It deserves a place in history as the only invention too farsighted even for PARC’s Computer Science Lab. And all because it thought in color.

The occasion was the broadcast of a television program about the artistic avant-garde entitled Supervisions, which was produced by the Los Angeles public television station KCET. Smith’s and Shoup’s work on Superpaint had started to win wide notice outside PARC, thanks in part to a tape called “Vidbits” which Smith had compiled from clips of his best work for playing to artists’ gatherings all around California. After one such showing, KCET commissioned the two of them to supply some brief color-cycling effects for Supervisions. They had scrupulously insisted that the producers give Xerox screen credit, assuming that the parent company would appreciate the honor. Instead, Taylor marched into the video lab a day or two after the broadcast and buttonholed Smith. “Xerox wants their logo off every piece of tape,” he said. “Right now.

I imagine that if I had been a male student my name might have been mentioned in class or that the professor might have encouraged my career in computer science, or perhaps offered me an opportunity in his or a colleague’s lab. This is why I get deeply angry when famous men (like Larry Summers, whom I will come to below) espouse the idea that women as a group are innately less good at science than men but say that of course they do not discriminate against individual talented women. They fail to miss the basic point that in the face of pervasive negative stereotyping, talented women will not be recognized. Such negative stereotyping is not supported by any data and is deeply harmful to all women

AMERICAN WHEAT OR RYE BEER Refreshing wheat or rye beers can display more hop character and less yeast character than their German cousins. This is a beginner-level style that can be brewed by extract or all-grain methods. Ferments at 65° F (18° C). OG FG IBU Color Alcohol 1.040-1.055 (10-13.6 °P) 1.008-1.013 (2.1-3.3 °P) 15-30 3-6 SRM 6-12 EBC 4-5.5% ABV 3.2-4.3% ABW Keys to Brewing American Wheat or Rye Beer: This easy-drinking beer style usually has a subtly grainy wheat character, slightly reminiscent of crackers. The hop flavor and aroma are more variable, with some versions having no hop character, while others have a fairly noticeable citrus or floral flair. Even when the hops are more prominent, they should not be overwhelming, and the hop bitterness should be balanced. The rye version of this style has a slight spicy, peppery note from the addition of rye in place of some or all of the wheat. The key mistake many brewers make is in assuming that American wheat beer should be similar to German hefeweizen. However, this style should not have the clove and banana character of a hefeweizen. This beer should not be as malty (bready) as a German hefeweizen, either, so all-grain brewers will want to use a less malty American two-row malt. To get the right fermentation profile, it is important to use a fairly neutral yeast strain, one that doesn’t produce a lot of esters like the German wheat yeasts do. While you can substitute yeast like White Labs WLP001 California Ale, Wyeast 1056 American Ale, or Fermentis Safale US-05, a better choice is one that provides some crispness, such as an altbier or Kölsch yeast, and fermentation at a cool temperature. RECIPE: KENT'S HOLLOW LEG It was the dead of winter and I was in Amarillo, Texas, on a business trip with Kent, my co-worker. That evening at dinner I watched as Kent drank a liter of soda, several glasses of water, and three or four liters of American wheat beer. I had a glass of water and one liter of beer, and I went to the bathroom twice. Kent never left the table. When I asked Kent about his superhuman bladder capacity, he thought it was due to years of working as a programmer glued to his computer and to the wonderful, easy-drinking wheat beer. This recipe is named in honor of Kent’s amazing bladder capacity. This recipe has a touch more hop character than many bottled, commercial examples on the market, but a lot less than some examples you might find. If you want less hop character, feel free to drop the late hop additions. If you really love hops and want to make a beer with lots of hop flavor and aroma, increase the late hop amounts as you see fit. However, going past the amounts listed below might knock it out of consideration in many competitions for being “too hoppy for style,” no matter how well it is brewed. OG: 1.052 (12.8 °P) FG: 1.012 (3.0 °P) ADF: 77% IBU: 20 Color: 5 SRM (10 EBC) Alcohol: 5.3% ABV (4.1% ABW) Boil: 60 minutes Pre-Boil Volume: 7 gallons (26.5L) Pre-Boil Gravity: 1.044 (11.0 °P) Extract Weight Percent Wheat LME (4 °L) 8.9 lbs. (4.03kg) 100 Hops   IBU Willamette 5.0% AA, 60 min. 1.0 oz. (28g) 20.3 Willamette 5.0% AA, 0 min. 0.3 oz. (9g) 0 Centennial 9.0% AA, 0 min. 0.3 oz. (9g) 0 Yeast White Labs WLP320 American Hefeweizen, Wyeast 1010 American Wheat, or Fermentis Safale US-05 Fermentation and Conditioning Use 10 grams of properly rehydrated dry yeast, 2 liquid yeast packages, or make a starter. Ferment at 65° F (18° C). When finished, carbonate the beer to approximately 2.5 volumes. All-Grain Option Replace the wheat extract with 6 lbs. (2.72kg) American two-row malt and 6 lbs. (2.72kg) wheat malt. Mash at 152° F (67° C). Rye Option This beer can also be made with a portion of malted rye. The rye gives the beer a slightly spicy note and adds a certain creamy mouthfeel. Replace the wheat extract with 6 lbs. (2.72kg) American two-row malt, 3.75 lbs. (1.70kg) rye malt, and 3 lbs. (1.36kg) wheat malt. Mash at 152° F (67° C).

As the Model S fever gripped Silicon Valley, I visited Ford’s small research and development lab in Palo Alto. The head of the lab at the time was a ponytailed, sandal-wearing engineer named T. J. Giuli, who felt very jealous of Tesla. Inside of every Ford were dozens of computing systems made by different companies that all had to speak to each other and work as one. It was a mess of complexity that had evolved over time, and simplifying the situation would prove near impossible at this point, especially for a company like Ford, which needed to pump out hundreds of thousands of cars per year and could not afford to stop and reboot. Tesla, by contrast, got to start from scratch and make its own software the focus of the Model S. Giuli would have loved the same opportunity. “Software is in many ways the heart of the new vehicle experience,” he said. “From the powertrain to the warning chimes in the car, you’re using software to create an expressive and pleasing environment. The level of integration that the software has into the rest of the Model S is really impressive. Tesla is a benchmark for what we do here.” Not long after this chat, Giuli left Ford to become an engineer at a stealth start-up. There

Your wife?” “Right.” “What does she do?” Tracy asked. “She works for a janitorial company; they clean the buildings downtown.” “She works nights?” Kins said. “Yeah.” “Do you have kids?” Tracy asked. “A daughter.” “Who watches your daughter when you and your wife are working nights?” “My mother-in-law.” “Does she stay at your house?” Tracy said. “No, my wife drops her off on her way to work.” “So nobody was at home when you got there Sunday night?” Bankston shook his head. “No.” He sat up again. “Can I ask a question?” “Sure.” “Why are you asking me these questions?” “That’s fair,” Kins said, looking to Tracy before answering. “One of our labs found your DNA on a piece of rope left at a crime scene.” “My DNA?” “It came up in the computer database because of your military service. The computer generated it, so we have to follow up and try to get to the bottom of it.” “Any thoughts on that?” Tracy said. Bankston squinted. “I guess I could have touched it when I wasn’t wearing my gloves.” Tracy looked to Kins, and they both nodded as if to say, “That’s plausible,” which was for Bankston’s benefit. Her instincts were telling her otherwise. She said, “We were hoping there’s a way we could determine where that rope was delivered, to which Home Depot.” “I wouldn’t know that,” Bankston said. “Do they keep records of where things are shipped? I mean, is there a way we could match a piece of rope to a particular shipment from this warehouse?” “I don’t know. I wouldn’t know how to do that. That’s computer stuff, and I’m strictly the labor, you know?” “What did you do in the Army?” Kins asked. “Advance detail.” “What does advance detail do?” “We set up the bases.” “What did that entail?” “Pouring concrete and putting up the tilt-up buildings and tents.” “So no combat?” Kins asked. “No.” “Are those tents like those big circus tents?” Tracy asked. “Sort of like that.” “They still hold them up with stakes and rope?” “Still do.” “That part of your job?” “Yeah, sure.” “Okay, listen, David,” Tracy said. “I know you were in the police academy.” “You do?” “It came up on our computer system. So I’m guessing you know that our job is to eliminate suspects just as much as it is to find them.” “Sure.” “And we got your DNA on a piece of rope found at a crime scene.” “Right.” “So I have to ask if you would you be willing to come in and help us clear you.” “Now?” “No. When you get off work; when it’s convenient.” Bankston gave it some thought. “I suppose I could come in after work. I get off around four. I’d have to call my wife.” “Four o’clock works,” Tracy said. She was still trying to figure Bankston out. He seemed nervous, which wasn’t unexpected when two homicide detectives came to your place of work to ask you questions, but he also seemed to almost be enjoying the interaction, an indication that he might still be a cop wannabe, someone who listened to police and fire scanners and got off on cop shows. But it was more than his demeanor giving her pause. There was the fact that Bankston had handled the rope, that his time card showed he’d had the opportunity to have killed at least Schreiber and Watson, and that he had no alibi for those nights, not with his wife working and his daughter with his mother-in-law. Tracy would have Faz and Del take Bankston’s photo to the Dancing Bare and the Pink Palace, to see if anyone recognized him. She’d also run his name through the Department of Licensing to determine what type of car he drove. “What would I have to do . . . to clear me?” “We’d like you to take a lie detector test. They’d ask you questions like the ones we just asked you—where you work, details about your job, those sorts of things.” “Would you be the one administering the test?” “No,” Tracy said. “We’d have someone trained to do that give you the test, but both Detective Rowe and I would be there to help get you set up.” “Okay,” Bankston said. “But like I said, I have

Biology doesn't know in advance what the end product will be; there's no Stuffit Compressor to convert a human being into a genome. But the genome itself is very much akin to a compression scheme, a terrifically efficient description of how to build something of great complexity-perhaps more efficient than anything yet developed in the labs of computer scientists (never mind the complexities of the brain, there are trillions of cells in the rest of the body, and they are all supervised by the same 30,000-gene genome). And although there is no counterpart in nature to a program that compresses a picture into a compact description, there is a natural counterpart to the program that decompresses the compressed encoding, and that's the cell. Genome in, organism out. Through the logic of gene expression, cells are self-regulating factories that translate genomes into biological structure.

Nicholas Negroponte, the founder of the Media Lab at MIT, told me that during a meeting with some of the Internet pioneers in 1993 he asked how many computers they believed there would be in the year 2000. The highest number he was given was 30 million. In reality there were going to be 500 million.

In universities and pharmaceutical labs around the world, computer scientists and computational biologists are designing algorithms to sift through billions of gene sequences, looking for links between certain genetic markers and diseases. The goal is to help us sidestep the diseases we're most likely to contract and to provide each one of us with a cabinet of personalized medicines. Each one should include just the right dosage and the ideal mix of molecules for our bodies. Between these two branches of research, genetic and behavioral, we're being parsed, inside and out. Even the language of the two fields is similar. In a nod to geneticists, Dishman and his team are working to catalog what they call our "behavioral markers." The math is also about the same. Whether they're scrutinizing our strands of DNA or our nightly trips to the bathroom, statisticians are searching for norms, correlations, and anomalies. Dishman prefers his behavioral approach, in part because the market's less crowded. "There are a zillion people looking at biology," he says, "and too few looking at behavior." His gadgets also have an edge because they can provide basic alerts from day one. The technology indicating whether a person gets out of bed, for example, isn't much more complicated than the sensor that automatically opens a supermarket door. But that nugget of information is valuable. Once we start installing these sensors, and the electronics companies get their foot in the door, the experts can start refining the analysis from simple alerts to sophisticated predictions-perhaps preparing us for the onset of Parkinson's disease or Alzheimer's.

In universities and pharmaceutical labs around the world, computer scientists and computational biologists are designing algorithms to sift through billions of gene sequences, looking for links between certain genetic markers and diseases. The goal is to help us sidestep the diseases we're most likely to contract and to provide each one of us with a cabinet of personalized medicines. Each one should include just the right dosage and the ideal mix of molecules for our bodies. Between these two branches of research, genetic and behavioral, we're being parsed, inside and out. Even the language of the two fields is similar. In a nod to geneticists, Dishman and his team are working to catalog what they call our "behavioral markers." The math is also about the same. Whether they're scrutinizing our strands of DNA or our nightly trips to the bathroom, statisticians are searching for norms, correlations, and anomalies. Dishman prefers his behavioral approach, in part because the market's less crowded. "There are a zillion people looking at biology," he says, "and too few looking at behavior." His gadgets also have an edge because they can provide basic alerts from day one. The technology indicating whether a person gets out of bed, for example, isn't much more complicated than the sensor that automatically opens a supermarket door. But that nugget of information is valuable. Once we start installing these sensors, and the electronics companies get their foot in the door, the experts can start refining the analysis from simple alerts to sophisticated predictions-perhaps preparing us for the onset of Parkinson's disease or Alzheimer's.

Noll tried to register Gaussian Quadratic with the US Copyright Office at the Library of Congress, another body perplexed by the works on display. His request was originally denied “since a machine had generated the work.”10 He explained that a human being had written the program that, through a mix of randomness and order, generated the work. The Library of Congress again declined: randomness was unacceptable. Noll finally argued that although the numbers produced by the program appeared random, “the algorithm generating them was perfectly mathematical and not random at all,” and the work was finally patented.

Computing is not about computers any more. It is about living. Nicholas Negroponte, co-founder MIT Media Labs

Quite a setup you have down here.” Martin had never visited the computer lab before and had not imagined that it was so extensive. “It gives me a strange feeling to be in this room,” he admitted. “I went to med school here and back in sixty-one, I took

The hallmark of originality is rejecting the default and exploring whether a better option exists. I’ve spent more than a decade studying this, and it turns out to be far less difficult than I expected. The starting point is curiosity: pondering why the default exists in the first place. We’re driven to question defaults when we experience vuja de, the opposite of déjà vu. Déjà vu occurs when we encounter something new, but it feels as if we’ve seen it before. Vuja de is the reverse—we face something familiar, but we see it with a fresh perspective that enables us to gain new insights into old problems. Without a vuja de event, Warby Parker wouldn’t have existed. When the founders were sitting in the computer lab on the night they conjured up the company, they had spent a combined sixty years wearing glasses. The product had always been unreasonably expensive. But until that moment, they had taken the status quo for granted, never questioning the default price. “The thought had never crossed my mind,” cofounder Dave Gilboa says. “I had always considered them a medical purchase. I naturally assumed that if a doctor was selling it to me, there was some justification for the price.” Having recently waited in line at the Apple Store to buy an iPhone, he found himself comparing the two products. Glasses had been a staple of human life for nearly a thousand years, and they’d hardly changed since his grandfather wore them. For the first time, Dave wondered why glasses had such a hefty price tag. Why did such a fundamentally simple product cost more than a complex smartphone? Anyone could have asked those questions and arrived at the same answer that the Warby Parker squad did. Once they became curious about why the price was so steep, they began doing some research on the eyewear industry. That’s when they learned that it was dominated by Luxottica, a European company that had raked in over $7 billion the previous year. “Understanding that the same company owned LensCrafters and Pearle Vision, Ray-Ban and Oakley, and the licenses for Chanel and Prada prescription frames and sunglasses—all of a sudden, it made sense to me why glasses were so expensive,” Dave says. “Nothing in the cost of goods justified the price.” Taking advantage of its monopoly status, Luxottica was charging twenty times the cost. The default wasn’t inherently legitimate; it was a choice made by a group of people at a given company. And this meant that another group of people could make an alternative choice. “We could do things differently,” Dave suddenly understood. “It was a realization that we could control our own destiny, that we could control our own prices.” When we become curious about the dissatisfying defaults in our world, we begin to recognize that most of them have social origins: Rules and systems were created by people. And that awareness gives us the courage to contemplate how we can change them. Before women gained the right to vote in America, many “had never before considered their degraded status as anything but natural,” historian Jean Baker observes. As the suffrage movement gained momentum, “a growing number of women were beginning to see that custom, religious precept, and law were in fact man-made and therefore reversible.

I kicked my flip-flops off and pulled my knees to my chest. Mason sprawled next to me, his head propped on a pillow. His legs, which almost hung off the end of the tiny bed, were crossed at the ankle. “How was work?” I asked. He rolled his eyes and grumbled about the students who used the computer lab, compatibility issues and a whole bunch of other stuff I didn’t understand. Being that he was a computer science major, he was always frustrated by questions he found dumb. I tried to be supportive by letting him vent, but I also enjoyed giving him a hard time about it. I stared blankly at him. “I’m sorry, you’re speaking nerd again.” “You’re hilarious. Considering you’re fluent in it.” He made his voice girly. “Did you hear about the nucleotide enzymatic compound synthesis?” I bit back my smile. “That doesn’t even make sense. And I don’t sound like that. But I am impressed you remembered all that from Biology.” “You have no idea how long I’ve been waiting to use all those words in one sentence.” I laughed as he grinned proudly

Nicholas Negroponte, head of MIT’s Media Lab, once quipped in the 1990s that the urinal in the men’s restroom was smarter than his computer because it knew he was there and would flush when he left, while his computer had no idea he was sitting in front of it all day. That

we have a Gene-IE to grant us wishes,” he said. Darlene brushed past in her white coat. To my surprise, Eliza was at the computer desk behind the lab door and looked up at me. “You took your time,” she said with a glint in her green eyes. “Leaving us to do all the work.” I had an overwhelming urge to hug her, which I didn’t fight. She responded very tightly, then slowly broke away. “What are you doing standing

tutor in the computer lab. Claire was an art history major. She had never been good at math. Or at least she’d never tried to be, which was the same thing. She could vividly remember the first time she’d sat down with Paul and gone over one of her assignments.

Martin explains that working with architects in the Arup SoundLab has led to improved collaborations: 'We can relate to architects much better. If we are brought on board at the ideal time, which is if we are brought on board at concept, we can sit with the architect and say...this is what you have to work with. They can hear and they can understand it. Then from their first ideas and concepts they are much more willing to work with us when we talk to them about shape and form.

Martin's distinction between the approach to acoustics characterized by the Sabine formula and his own approach, using the Arup SoundLab, recalls the sociologist Max Weber's distinction between the ethics of the scientist and those of the politician. Weber explains the vocation of science as the pursuit of clarity. Scientists follow an "ethic of ultimate ends," in which the ends justify the means. "The believer in an ethic of ultimate ends feels responsibility only for seeing to it that the flame of pure intentions is not squelched." Weber contrasts this with the politician's "ethic of responsibility." For Weber, being a politician means giving priority to the legitimacy of the means over the end. In their shift from the optimization of reverberation time to the pursuit of a consensus in the Arup SoundLab, acousticians have traded the ethics of the scientist for those of the politician. By focusing on a collaborative means of examining architectural acoustics, Martin and his colleagues have created a place for themselves as engaged co-designers rather than objective scientists.

In the early twenty-first century, as criminals figured out ways to monetize their malicious software through identity theft and other techniques, the number of new viruses began to soar. By 2015, the volume had become astonishing. In 2010, the German research institute AV-Test had assessed that there were forty-nine million strains of computer malware in the wild. By 2011, the antivirus company McAfee reported it was identifying two million new pieces of malware every month. In the summer of 2013, the cyber-security firm Kaspersky Lab reported it identified and isolated nearly 200,000 new malware samples every single day.

The Tinkering School. More of a lab than a school, this summer program, created by computer scientist Gever Tulley, lets children from seven to seventeen play around with interesting stuff and build cool things.

Marc Goodman is a cyber crime specialist with an impressive résumé. He has worked with the Los Angeles Police Department, Interpol, NATO, and the State Department. He is the chief cyber criminologist at the Cybercrime Research Institute, founder of the Future Crime Institute, and now head of the policy, law, and ethics track at SU. When breaking down this threat, Goodman sees four main categories of concern. The first issue is personal. “In many nations,” he says, “humanity is fully dependent on the Internet. Attacks against banks could destroy all records. Someone’s life savings could vanish in an instant. Hacking into hospitals could cost hundreds of lives if blood types were changed. And there are already 60,000 implantable medical devices connected to the Internet. As the integration of biology and information technology proceeds, pacemakers, cochlear implants, diabetic pumps, and so on, will all become the target of cyber attacks.” Equally alarming are threats against physical infrastructures that are now hooked up to the net and vulnerable to hackers (as was recently demonstrated with Iran’s Stuxnet incident), among them bridges, tunnels, air traffic control, and energy pipelines. We are heavily dependent on these systems, but Goodman feels that the technology being employed to manage them is no longer up to date, and the entire network is riddled with security threats. Robots are the next issue. In the not-too-distant future, these machines will be both commonplace and connected to the Internet. They will have superior strength and speed and may even be armed (as is the case with today’s military robots). But their Internet connection makes them vulnerable to attack, and very few security procedures have been implemented to prevent such incidents. Goodman’s last area of concern is that technology is constantly coming between us and reality. “We believe what the computer tells us,” says Goodman. “We read our email through computer screens; we speak to friends and family on Facebook; doctors administer medicines based upon what a computer tells them the medical lab results are; traffic tickets are issued based upon what cameras tell us a license plate says; we pay for items at stores based upon a total provided by a computer; we elect governments as a result of electronic voting systems. But the problem with all this intermediated life is that it can be spoofed. It’s really easy to falsify what is seen on our computer screens. The more we disconnect from the physical and drive toward the digital, the more we lose the ability to tell the real from the fake. Ultimately, bad actors (whether criminals, terrorists, or rogue governments) will have the ability to exploit this trust.

Gosper had disdained NASA’s human-wave approach toward things. He had been adamant in defending the AI lab’s more individualistic form of hacker elegance in programming, and in computing style in general. But now he saw how the real world, when it got its mind made up, could have an astounding effect. NASA had not applied the Hacker Ethic, yet it had done something the lab, for all its pioneering, never could have done. Gosper realized that the ninth-floor hackers were in some sense deluding themselves, working on machines of relatively little power compared to the computers of the future — yet still trying to do it all, change the world right there in the lab. And

Mei sat down at her desk in the lab and booted her computer. The room was quiet but for the whir of the case fans and coolers of the computer. The mechanical platters buzzed softly as the tiny magnetic head leapt around to access her hard drive, the platters spinning an upwards of 10,000 times per minute. She loved the sounds of the machinery, could diagnose a sick computer just by the sounds. She

I knew middle school was going to be challenging, but I never expected to end up DEAD in the computer lab, wearing a SUPERHERO COSTUME, with four slices of PIZZA stuck to my BUTT!

As one Great Group after another has shown, talented people don’t need fancy facilities. It sometimes seems that any old garage will do. But they do need the right tools. The leaders of PARC threatened to quit if the lab was not allowed to build the computer it needed, rather than accept an inferior technology. Cutting-edge technology is often a key element in creative collaboration. The right tools become part of the creative process.

As a Principal IT Consultant specializing in back-end enterprise tasks using Java technologies, Dr. Emma Quindazzi leverages her applied application focused doctorate, DSc. in Computer Science, emphasizing Enterprise Information Systems. Beyond her professional roles, she volunteers at the AI Wildlife Research Lab, recognized for her teaching excellence and software engineering prowess. In her leisure, Emma indulges in studying animal behavior, family bonding, sailing, flying, and immersing herself in music and literature.

It’s just like I always say. Better to be lucky than good.” I think about fingerprints and DNA and a computer team poring over evidence in a lab downtown. “No, McDuff,” I reply. “It’s better to be both.

They said, ‘We just gave you the 8086 last week! How could you report a bug already?’”, Tesler recalled. But Intel had not reckoned with PARC’s do-it-yourself mentality. Years earlier the lab had purchased a rare million-dollar machine known as a Stitchweld, which could turn out printed circuit boards overnight from a digital schematic prepared on Thacker’s SIL program. “It turned out that no one else using the 8086 had Stitchwelds. Everyone else was going through complicated board designs, so they wouldn’t know for months if there was a bug. But at Xerox we gave them that feedback in a few days.

Early in his Bell Labs career, Shannon had begun to conceive of his employer’s system—especially its vast arrangement of relays and switches that automatically connected callers—as more than a communications network. He saw it as an immense computer that was transforming and organizing society. This was not yet a conventional view, though it was one that Shockley, too, would soon adopt. As Shannon put it, the system and its automatic switching mechanisms was “a really beautiful example of a highly complex machine.

Most frustrating and important, Wilson said, assisted living isn’t really built for the sake of older people so much as for the sake of their children. The children usually make the decision about where the elderly live, and you can see it in the way that places sell themselves. They try to create what the marketers call “the visuals”—the beautiful, hotel-like entryway, for instance, that caught Shelley’s eye. They tout their computer lab, their exercise center, and their trips to concerts and museums—features that speak much more to what a middle-aged person desires for a parent than to what the parent does. Above all, they sell themselves as safe places. They almost never sell themselves as places that put a person’s choices about how he or she wants to live first and foremost. Because it’s often precisely the parents’ cantankerousness and obstinacy about the choices they make that drive children to bring them on the tour to begin with. Assisted living has become no different in this respect than nursing homes.

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For all these reasons, the technology couldn’t attract enough users to attract even more users. “To start up a service, you have to think about: I have one, you don’t have one—so I can’t talk to you,” Irwin Dorros says. “So I can only talk to you if you have one. So how do you get a critical mass of people that have them?” Many years later, a computer engineer named Robert Metcalfe would surmise that the value of a networked device increases dramatically as the number of people using the network grows. The larger the network, in other words, the higher the value of a device on that network to each user.36 This formulation—sometimes known as Metcalfe’s law—can help explain the immense appeal of the telephone system and Internet. However, the smaller the network, the lower the value of a device to each user. Picturephone’s network was minuscule.

So often,” says Ian Ross, who worked in Jack Morton’s department at Bell Labs doing transistor development in the 1950s, “the original concept of what an innovation will do”—the replacement of the vacuum tube, in this case—“frequently turns out not to be the major impact.”1 The transistor’s greatest value was not as a replacement for the old but as an exponent for the new—for computers, switches, and a host of novel electronic technologies.

The good thing about the transistor was that by the late 1950s it was becoming smaller and smaller as well as more and more reliable. The bad thing was that an electrical circuit containing thousands of tiny transistors, along with other elements such as resistors and capacitors, had to be interconnected with thousands of tiny wires. As Ian Ross describes it, “as you built more and more complicated devices, like switching systems, like computers, you got into millions of devices and millions of interconnections. So what should you do?

During those same years, there were other achievements at Bell Labs that would, in time, alter the world. One occurred when several computer scientists at Murray Hill got together to write a revolutionary computer operating system they called Unix, which was written in a new computer language called C.

Nine years later another computer hit 1.8 teraflops. But instead of simulating nuclear explosions, it was devoted to drawing them and other complex graphics in all their realistic, real-time, three-dimensional glory. It did this not for physicists, but for video game players. This computer was the Sony PlayStation 3, which matched the ASCI Red in performance, yet cost about five hundred dollars, took up less than a tenth of a square meter, and drew about two hundred watts.11 In less than ten years exponential digital progress brought teraflop calculating power from a single government lab to living rooms and college dorms all around the world. The PlayStation 3 sold approximately 64 million units.

As I listened to them, I came to the unsettling conclusion that the dehumanization they experienced was created at least in part in computer labs from Seattle to Beijing.

Pierce was thinking about the New York fair around the same time that a modest display of Bell Labs innovations was being demonstrated at Seattle’s Century 21 Exposition, which was being marked by the construction of a huge “space needle” on the city’s fairgrounds. At the Seattle fair visitors could ride a monorail to a Bell exhibit intimating a future of startling convenience: phones with speedy touch-tone buttons (which would soon replace dials), direct long-distance calling (which would soon replace operators), and rapid electronic switching (which would soon be powered by transistors). A visitor could also try something called a portable “pager,” a big, blocky device that could alert doctors and other busy professionals when they received urgent calls.2 New York’s fair would dwarf Seattle’s. The crowds were expected to be immense—probably somewhere around 50 or 60 million people in total. Pierce and David’s 1961 memo recommended a number of exhibits: “personal hand-carried telephones,” “business letters in machine-readable form, transmitted by wire,” “information retrieval from a distant computer-automated library,” and “satellite and space communications.” By the time the fair opened in April 1964, though, the Bell System exhibits, housed in a huge white cantilevered building nicknamed the “floating wing,” described a more conservative future than the one Pierce and David had envisioned.

The transistor was the ideal digital tool. With tiny bursts of electricity, it could be switched on or off—in essence, turned into a yes or no, or a 1 or 0—at speeds measured in billionths of a second. Thus in addition to being an amplifier, a clump of transistors could be linked together to enable a logical decision (and thereby process information). Or a clump could be linked together to help represent bits of information (and thereby remember information). To put hundreds, or thousands, or tens of thousands of the devices alongside one another (the notion that billions would one day fit together was still unimaginable) might allow for extraordinary possibilities. It was a “wondrous coincidence,” as Bill Baker described it, “that all of human knowledge and experience can be completely and accurately expressed in binary digital terms.”2 As usual, Shannon was ahead of his colleagues. But in only a few years, by the late 1950s, Baker, too, viewed the future of digital computing and that of human society as wholly interrelated.

You shouldn’t make fun of that. It’s not a joke.” She walked away before I could reply. I stayed in my seat until everyone had gone, pretending the zipper on my coat was stuck so I could avoid looking anyone in the eye. Then I went straight to the computer lab to look up the word “Holocaust.” I

An experimental physics lab is probably unlike any other room you’ve been in before. The lighting is harsh, of course, aggressively bright and beyond the reach of aesthetic concerns. There are sounds of machines, a harmonic hum, sometimes just from fans on computer equipment as opposed to any motorized parts. There’s never any bespoke sound absorbers, so the machines have a sonic clarity that seems intentional, cranked up for some postindustrial experimental orchestra.

To maximize innovation, maximize the fringes. Encourage borders, outskirts, and temporary isolation where the voltage of difference can spark the new. The principle of skunk works plays a vital role in the network economy. By definition a network is one huge edge. It has no fixed center. As the network grows it holds increasing opportunities for protected backwaters where innovations can hatch, out of view but plugged in. Once fine-tuned, the innovation can replicate wildly. The global dimensions of the network economy means that an advance can be spread quickly and completely through the globe. The World Wide Web itself was created this way. The first software for the web was written in the relative obscurity of an academic research station in Geneva, Switzerland. Once it was up and running in their own labs in 1991, it spread within six months to computers all around the world.

We hired people with fire in their eyes,” said one lab member, while another noted, “The people here all have track records and are used to dealing with lightning in both hands.

But if IOTA lost the confidence of some of the most respected cryptographers in the blockchain community, it continued to generate enthusiasm among a variety of big-name enterprises. That’s perhaps because, quite apart from how badly or otherwise it developed and managed its cryptography, the IOTA team’s economic model is enticing. If its cryptographic flaws can be fixed, the tangle idea could in theory be far less taxing and expensive in terms of computing power than Bitcoin and Ethereum’s methods, which require every computer in their massive networks of validators to process and confirm the entire list of new transactions in each new block. German engineering and electronics giant Bosch has been running a range of experiments with IOTA, including one involving payments between self-driving trucks arranged in an energy-saving linear “platoon.” The idea is that the trucks at the back that are enjoying the benefits of the slipstream would pay IOTA tokens to those at the front to compensate them for bearing the bulk of energy costs in creating that slipstream. Meanwhile, IOTA and Bosch are both part of a consortium called the Trusted IoT Alliance that’s committed to building and securing a blockchain infrastructure for the industry. Other members include Foxconn, Cisco, BNY Mellon, and a slew of blockchain-based startups, such as supply-chain provider Skuchain and Ethereum research lab ConsenSys.

A study by psychologists at the University of Washington, for example, asked pairs of four-year-old children to play on a swing set apparatus installed in their lab; the researchers then discreetly manipulated whether the kids swung in unison or out of sync. After getting down from the swings, the preschoolers who had swung in time with their partners were more likely to cooperate with those same partners on a subsequent set of tasks. Comparable results were found among eight-year-olds who experienced synchronized play on a computer game; afterwards they reported feeling a greater sense of similarity and closeness to their partners than did participants who also played the game but did so out of sync with their peers. Studies conducted with adults show the same: moving in sync makes us better collaborators.

The dramatic transformation that deep learning promises to bring to the global economy won’t be delivered by isolated researchers producing novel academic results in the elite computer science labs of MIT or Stanford. Instead, it will be delivered by down-to-earth, profit-hungry entrepreneurs teaming up with AI experts to bring the transformative power of deep learning to bear on real-world industries.

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

Yes, please… Wow, you type fast! I knew you’d be good at computer stuff.” We were in the school computer lab. Mimimi was appreciating the typing skills I’d honed in online gaming chat rooms.

we walked into the lab, Mrs. Yonkers was sitting on top of her desk, and her computer was on her chair. That was weird. You’re supposed to sit on your chair with the computer on your desk. It was also weird that Mrs.