Ibm Best Quotes

But Mandelbrot continued to feel oppressed by France’s purist mathematical establishment. “I saw no compatibility between a university position in France and my still-burning wild ambition,” he writes. So, spurred by the return to power in 1958 of Charles de Gaulle (for whom Mandelbrot seems to have had a special loathing), he accepted the offer of a summer job at IBM in Yorktown Heights, north of New York City. There he found his scientific home. As a large and somewhat bureaucratic corporation, IBM would hardly seem a suitable playground for a self-styled maverick. The late 1950s, though, were the beginning of a golden age of pure research at IBM. “We can easily afford a few great scientists doing their own thing,” the director of research told Mandelbrot on his arrival. Best of all, he could use IBM’s computers to make geometric pictures. Programming back then was a laborious business that involved transporting punch cards from one facility to another in the backs of station wagons.

On May 3, 1997, a chess match began between Deep Blue, a chess computer built by IBM, and Garry Kasparov, the world chess champion and possibly the best human player in history. Newsweek billed the match as “The Brain’s Last Stand.” On May 11, with the match tied at 2½–2½, Deep Blue defeated Kasparov in the final game. The media went berserk. The market capitalization of IBM increased by $18 billion overnight. AI had, by all accounts, achieved a massive breakthrough.

In this office right now I am thinking about how long it would take a corpse to disintegrate right in this office. In this office these are the things I fantasize about while dreaming: Eating ribs at Red, Hot and Blue in Washington, D.C. If I should switch shampoos. What really is the best dry beer? Is Bill Robinson an overrated designer? What’s wrong with IBM? Ultimate luxury. Is the term “playing hardball” an adverb? The fragile peace of Assisi. Electric light. The epitome of luxury. Of ultimate luxury. The bastard’s wearing the same damn Armani linen suit I’ve got on.

Then, in the end, the leader makes the call. It’s conflict and debate leading to an executive decision. No major decision we’ve studied was ever taken at a point of unanimous agreement. There was always some disagreement in the air. Our research showed that before a major decision, you would see significant debate. But after the decision, people would unify behind that decision to make it successful. Again, and I can’t stress this too much, it all begins with having the right people—those who can debate in search of the best answers but who can then set aside their disagreements and work together for the success of the enterprise.

There were movies to go see at the Gem, which has long since been torn down; science fiction movies like Gog with Richard Egan and westerns with Audie Murphy (Teddy saw every movie Audie Murphy made at least three times; he believed Murphy was almost a god) and war movies with John Wayne. There were games and endless bolted meals, lawns to mow, places to run to, walls to pitch pennies against, people to clap you on the back. And now I sit here trying to look through an IBM keyboard and see that time, trying to recall the best and the worst of that green and brown summer, and I can almost feel the skinny, scabbed boy still buried in this advancing body and hear those sounds. But

I think we're all just doing our best to survive the inevitable pain and suffering that walks alongside us through life. Long ago, it was wild animals and deadly poxes and harsh terrain. I learned about it playing The Oregon Trail on an old IBM in my computer class in the fourth grade. The nature of the trail has changed, but we keep trekking along. We trek through the death of a sibling, a child, a parent, a partner, a spouse; the failed marriage, the crippling debt, the necessary abortion, the paralyzing infertility, the permanent disability, the job you can't seem to land; the assault, the robbery, the break-in, the accident, the flood, the fire; the sickness, the anxiety, the depression, the loneliness, the betrayal, the disappointment, and the heartbreak. There are these moments in life where you change instantly. In one moment, you're the way you were, and in the next, you're someone else. Like becoming a parent: you're adding, of course, instead of subtracting, as it is when someone dies, and the tone of the occasion is obviously different, but the principal is the same. Birth is an inciting incident, a point of no return, that changes one's circumstances forever. The second that beautiful baby onto whom you have projected all your hopes and dreams comes out of your body, you will never again do anything for yourself. It changes you suddenly and entirely. Birth and death are the same in that way.

What’s the best thing you’ve done in your work and career? In business decision-making, certainly one of your highlights was licensing your computer operating system to IBM for almost no money, provided you could retain the right to license the system to other computer manufacturers as well. IBM was happy to agree because, after all, nobody would possibly want to compete with the most powerful company in the world, right? With that one decision, your system and your company became dominant throughout the world, and you, Bill Gates, were on your way to a net worth of more than $60 billion. Or maybe you’d like to look at your greatest career achievement from a different angle. Instead of focusing on the decision that helped you make so much money, maybe you’d like to look at the decision to give so much of it away. After all, no other person in history has become a philanthropist on the scale of Bill Gates. Nations in Africa and Asia are receiving billions of dollars in medical and educational support. This may not be as well publicized as your big house on Lake Washington with its digitalized works of art, but it’s certainly something to be proud of. Determining your greatest career achievement is a personal decision. It can be something obvious or something subtle. But it should make you proud of yourself when you think of it. So take a moment, then make your choice.

In a 1997 showdown billed as the final battle for supremacy between natural and artificial intelligence, IBM supercomputer Deep Blue defeated Garry Kasparov. Deep Blue evaluated two hundred million positions per second. That is a tiny fraction of possible chess positions—the number of possible game sequences is more than atoms in the observable universe—but plenty enough to beat the best human. According to Kasparov, “Today the free chess app on your mobile phone is stronger than me.” He is not being rhetorical. “Anything we can do, and we know how to do it, machines will do it better,” he said at a recent lecture. “If we can codify it, and pass it to computers, they will do it better.” Still, losing to Deep Blue gave him an idea. In playing computers, he recognized what artificial intelligence scholars call Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses. There is a saying that “chess is 99 percent tactics.” Tactics are short combinations of moves that players use to get an immediate advantage on the board. When players study all those patterns, they are mastering tactics. Bigger-picture planning in chess—how to manage the little battles to win the war—is called strategy. As Susan Polgar has written, “you can get a lot further by being very good in tactics”—that is, knowing a lot of patterns—“and have only a basic understanding of strategy.

Computers already have enough power to outperform people in activities we used to think of as distinctively human. In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov. Jeopardy!’s best-ever contestant, Ken Jennings, succumbed to IBM’s Watson in 2011. And Google’s self-driving cars are already on California roads today. Dale Earnhardt Jr. needn’t feel threatened by them, but the Guardian worries (on behalf of the millions of chauffeurs and cabbies in the world) that self-driving cars “could drive the next wave of unemployment.

Despite the challenges, Coca-Cola succeeded in the end. China has become Coca-Cola's third largest market in the world, after the United States and Mexico. It has invested over $5 billion in China. More important, Coca-Cola has blazed a trail for other foreign companies—Pepsi, KFC, McDonald's, Coors, Budweiser, IBM, Apple, Dell, Procter & Gamble, Walmart, Sheraton,

independent of the normal planning and budgeting processes, to allow bottom-up ideas to flourish. “No one has ever before brought together such a global and diverse set of business thought leaders on this scale to discuss the most pressing issues and opportunities of our age,” says Nick Donofrio, IBM’s executive vice president of innovation and technology. “We have companies literally knocking at the door and saying, ‘Give us your best and brightest ideas, and let’s work together to make them a reality.’ It’s a golden opportunity to create entirely new markets and partnerships.

The twelve management principles of IBM are: Principle #1 - The purpose and mission should be set clearly. Additionally noble and fair objective should be set. Principle #2 – Goals should be specific and when the targets are set, employees should be notified. Principle #3 – Your heart should always be full with strong and persistent passionate desire. Principle #4 – You should be the one who strives for the most. The tasks that you set should be reasonable, and you should work hard on completion. Principle #5 – Costs should be minimized and profit should be maximized. The profit should not be chased but the inflows and the outflows should be controlled. Principle #6 – Top management should be the one to set pricing strategy. They need to find the perfect balance between profitability and happy customers. Principle #7 – The business management requires strong will. Principle #8 - The manager should have corresponding mentality. Principle #9 – Every challenge should be faced with courage. Each challenge should be resolved in fair way. Principle #10 – Creativity should always be present. New stop to innovate and improve, otherwise you will not be able to compete in today’s tough world. Principle #11 – Never forget to be a human. You need to be kind, fair and sincere. Principle #12 – Never lose your hope. Be positive, happy, cheerful and keep your hopes alive. Deciding which way you want your company to go is essential for ensuring success. You can follow IBM’s example, or adapt these principles to fit your situation. I always recommend that you ensure that every employee knows your principles. Employees will feel more confident, secure and motivated if they start working in a company that knows what it wants, where it will be in 10 years, what should be done in order to reach the specific/or set goals, etc. Once you have your principles it is important that you follow them as well. Leading from the front is the best way to inspire those around you.

The fact is that one person’s growth stock is another’s value stock. Recently, the investment data company Lipper has reported that Citigroup, AIG and IBM are among the top 15 mutual fund holdings in both the large company “value” and “growth” categories. This brings us to our next point, which perhaps best explains why Marathon should never be labelled as a pure value investor. Our capital cycle process examines the effects of the creative and destructive forces of capitalism over time. A growth stock usually becomes a value stock after excess capital, lured in by large current profitability, brings about a decline in returns. When this becomes extreme, as was the case during the technology bubble, the resultant bust can turn growth stocks into value stocks almost overnight. The telecoms sector provides

When the U.S. Census Bureau sponsored a contest seeking the best automated counting device for its 1890 census, it was no surprise when Hollerith’s design won.

No one ever got fired for hiring IBM,” goes the old adage, describing a behavior completely borne out of fear. An employee in a procurement department, tasked with finding the best suppliers for a company, turns down a better product at a better price simply because it is from a smaller company or lesser-known brand. Fear, real or perceived, that his job would be on the line if something went wrong was enough to make him ignore the express purpose of his job, even do something that was not in the company’s best interest.

Pushkala’s team knew that top-down approaches like those used by Lou Gerstner and Steve Jobs would backfire in this company as, unlike IBM and Apple, AstraZeneca wasn’t in crisis—although revenue and profits fell between 2011 and 2016. AstraZeneca is also a decentralized company, in which local leaders have substantial authority to accept, modify, or ignore orders from on high. So, rather than telling people what to do, Pushkala’s team took “a player-coach” approach. They implemented some key companywide efforts, but believed their success hinged on the cumulative impact of small systemwide and local changes. Most employees would join the effort because they wanted to, not because they had to. And the team believed that many of the best solutions would be tailored for tackling distinct local problems. As Pushkala put it, “Let us not solve world hunger; let us start eating the elephant in small chunks.

The company even drew unlikely customers. From rural Arkansas, operating just five comically cheap-looking stores—a rounding error compared with the largest retailers—Sam Walton made his way to an IBM conference for retailers. While he shied away from investing anything in any emotional aspect of retailing, delivering the lowest prices meant mastering logistics and information. To one speaker at the conference, Abe Marks, modern retailing meant knowing exactly “how much merchandise is in the store? What’s selling and what’s not? What is to be ordered, marked down or replaced? . . . The more you turn your inventory, the less capital is required.” Altering his first impression, Marks found that Walton’s simpleton comportment masked his genius as a retailer, eventually calling him the “best utilizer of information that there’s ever been.” A little over two decades later, Sam Walton would become the richest man in America; he would attribute his competitive advantage to his investment in computing systems in his early days. The small-town merchant who expected that knowing his customers’ names or sponsoring the local Little League team would give him some enduring advantage simply didn’t understand the sport. American consumers, technocrats at heart, rewarded efficiency as reflected by the prices on the shelves, not the quaint sentiments of a friendly proprietor. To gain this efficiency, information systems were seen as vital.

It’s difficult to imagine that Artificial Intelligence will take the place of people but many believe that it’s only a short time before computers will outthink us. They already can beat our best chess players and have been able to out calculate us since calculators first came onto the scene. IBM’s Watson is on the cutting edge of Cognitive Computers, being used to out think our physicians but closer to home, for the greatest part; our cars are no longer assembled by people but rather robots. Our automobiles can be considered among our first robots, since they took the place of horses. Just after the turn of the last century when the population in the United States crossed the 100 M mark the number of horses came to 20M. Now we have a population of 325 M but only 9 M horses. You might ask what happened. Well back in 1915 there were 2.4 M cars but this jumped to 3.6 M in just one year. Although horses still out-numbered cars the handwriting was on the wall! You might think that this doesn’t apply to us but why not? The number of robots increase, taking the place of first our workers on the assembly line and then workers in the food industry and this takes us from tractors and combines on the farms to the cooking and serving hamburgers at your favorite burger joint. People are becoming redundant! That’s right we are becoming superfluous! Worldwide only 7 out of 100 people have college degrees and here in the United States only 40% of our working population possesses a sheep skin, although mine is printed on ordinary paper. With education becoming ever more expensive, we as a population are becoming ever more uneducated. A growing problem is that as computers and robots become smarter, as they are, we are no longer needed to be anything more than a consumer and where will the money come from for that? I recently read that this death spiral will run its course within 40 years! Nice statistics that we’re looking at…. Looking at the bright side of things you can now buy an atomically correct, life sized doll, as perhaps a robotic non-complaining, companion for under $120. In time these robotic beings will be able to talk back but hopefully there will be an off switch. As interesting as this sounds it will most likely not be for everyone, however it may appeal to some of our less capable, not to have to actually interface with real live people. The fact is that most people will soon outlive their usefulness! We as a society are being challenged and there will soon be little reason for our being. When machines make machines that can out think us; when we become dumb and superfluous, then what? Are we ready for this transition? It’s scary but If nothing else, it’s something to think about….

Charlie Chaplin exploited frustrations and fears about rapidly growing automation to make people laugh. It’s ironic that IBM once used his tramp character as an implied advertising testimonial for computers, because Chaplin’s character didn’t promote machines—he ridiculed them.

Many companies worked hard at going cheap on IT, and they eventually took a beating for it. Names like Sears, Sprint, BestBuy, and, more recently, Target, come to mind.

The best entrepreneurs create environments of stressful urgency. Entrepreneurs know that start-ups rarely get anything done in a relaxed, take-your-time environment. For example, Steve Jobs, the cofounder of Apple, was notorious for pushing his team beyond its limits by setting seemingly unrealistic timelines. As a result, his company created products quicker than they had ever imagined was possible and thus gained a huge competitive advantage over rival companies like IBM.

Consider, for example, IBM’s decision to outsource the microprocessor for its PC business to Intel, and its operating system to Microsoft. IBM made these decisions in the early 1980s in order to focus on what it did best—designing, assembling, and marketing computer systems. Given its history, these choices made perfect sense. Component suppliers to IBM historically had lived a miserable, profit-free existence, and the business press widely praised IBM’s decision to out-source these components of its PC. It dramatically reduced the cost and time required for development and launch. And yet in the process of outsourcing what it did not perceive to be core to the new business, IBM put into business the two companies that subsequently captured most of the profit in the industry. How could IBM have known in advance that such a sensible decision would prove so costly? More broadly, how can any executive who is launching a new-growth business, as IBM was doing with its PC division in the early 1980s, know which value-added activities are those in which future competence needs to be mastered and kept inside? 2

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

In Tsai's go‐go years, high‐flying stocks with​ positive momentum were all the rage. Polaroid, Xerox, IBM all traded at price‐to‐earnings ratios of more than 50. These expensive stocks were supported by explosively high growth rates. From 1964 to 1968, IBM, Polaroid, and Xerox grew their earnings per share at 88%, 22%, and 171%, respectively. Others like University Computing, Mohawk Data, and Fairchild Camera traded at several‐hundred times their trailing 12‐month earnings. The latter three and many others like them would go on to lose more than 80% in the 1969–1970 bear market. The Manhattan Fund was up almost 40% in 1967, more than double the Dow. But in 1968, he was down 7% and was ranked 299th out of 305 funds tracked by Arthur Lipper.16 When the market crash came, the people responsible were entirely unprepared. By 1969, half of the salesmen on Wall Street had only come into the business since 196217 and had seen nothing but a rising market. And when stocks turned, the highfliers that went up the fastest also came down the fastest. For example, National Student Marketing, which Tsai bought 122,000 shares for $5 million, crashed from $143 in December 1969 to $3.50 in July 1970.18 Between September and November 1929, $30 billion worth of stock value vanished; in the1969‐1970 crash, the loss was $300 billion!19 The gunslingers of the 1960s were thinking only about return and paid little attention to risk. This carefree attitude was a result of the market they were playing in. From 1950 through the end of 1965, the Dow was within 5% of its highs 66% of the time, and within 10% of its highs 87% of the time. There was virtually no turbulence at all. From 1950 to 1965, the only bear market was “The Kennedy Slide,” which chopped 27% off the S&P 500, and recovered in just over a year.

At the time,” he said, “I was young, and I looked younger. IBM had people around the table who were initially quite skeptical of me.” He explained that the first step in a sales meeting is having to blast through skepticism, and the best way to do that is by overwhelming people with your expertise. Gates would talk fast and dive immediately into the details—character sets, computer chips, programming languages, software platforms—to the point that it became undeniably clear he wasn’t just some kid.