Watson Ibm Quotes

(Thomas J. Watson Sr. of IBM followed the same rule: “I’m no genius,” he said. “I’m smart in spots—but I stay around those spots.”)

IBM experimented with adding Urban Dictionary data to its artificial intelligence system Watson, only to scrub it all out again when the computer started swearing at them.

A while back, I came across a line attributed to IBM founder Thomas Watson. If you want to achieve excellence, he said, you can get there today. As of this second, quit doing less-than-excellent work.

Goods in any storehouse are useless until somebody takes them out and puts them to the use they were meant for. That applies to what man stores away in his brain, too. —THOMAS J. WATSON, FORMER PRESIDENT OF IBM

A promising junior executive of IBM was involved in a risky venture for the company and managed to lose over $10 million in the gamble. It was a disaster. When Watson called the nervous executive into his office, the young man blurted out, 'I guess you want my resignation?' Watson said, 'You can't be serious. We've just spent $10 million educating you!

Watson, Sr., was running IBM, he decided they would never have more than four layers from the chairman of the board to the lowest level in the company. That may have been one of the greatest single reasons why IBM was successful.

And Thomas Watson, chairman of IBM, said in 1943, “I think there is a world market for maybe five computers.

Every time a seismic shift takes place in our economy, there are people who feel the vibrations long before the rest of us do, vibrations so strong they demand action—action that can seem rash, even stupid. Ferry owner Cornelius Vanderbilt jumped ship when he saw the railroads coming. Thomas Watson Jr., overwhelmed by his sense that computers would be everywhere even when they were nowhere, bet his father’s office-machine company on it: IBM. Jeffrey Preston Bezos had that same experience when he first peered into the maze of connected computers called the World Wide Web and realized that the future of retailing was glowing back at him.

… as I associated with more and more different types, I realized that to make it, you had to get along with almost everybody. If you dislike the people you work with, you’d better not show it. I learned that to be a good leader, I had to strike a delicate balance.

certain group of people in the United States tried an experiment. They tried the experiment of making a fortune without working, of making a fortune through the stock exchange. They extended the experiment until it exploded and all went down to earth.  “Aspects of World Trade” Thomas J. Watson Sr. July 31, 1930

As an IBM document describing the Watson technology points out: “We have noses that run, and feet that smell. How can a slim chance and a fat chance be the same, but a wise man and a wise guy are opposites?

If you want to increase your success rate, double your failure rate. Thomas J. Watson, Former Chairman and CEO of IBM

Thomas J. Watson, the former chairman of IBM, said, “Nothing so conclusively proves a man’s ability to lead others as what he does from day to day to lead himself.

Every time a seismic shift takes place in our economy, there are people who feel the vibrations long before the rest of us do, vibrations so strong they demand action—action that can seem rash, even stupid. Ferry owner Cornelius Vanderbilt jumped ship when he saw the railroads coming. Thomas Watson Jr., overwhelmed by his sense that computers would be everywhere even when they were nowhere, bet his father’s office-machine company on it: IBM. Jeffrey Preston Bezos had that same experience when he first peered into the maze of connected computers called the World Wide Web and realized that the future of retailing was glowing back at him.… Bezos’ vision of the online retailing universe was so complete, his Amazon.com site so elegant and appealing, that it became from Day One the point of reference for anyone who had anything to sell online. And that, it turns out, is everyone.

Under Armour. "Ahora estamos en el punto donde está ocurriendo un cambio y los consumidores están demandando más de esta información. Esta asociación con IBM nos permitirá aportar valor al consumidor de manera inédita, ya que integramos la tecnología de aprendizaje de máquinas de IBM Watson con los robustos datos de la comunidad Connected Fitness de Under Armour, la comunidad digital más grande del mundo de más de 160 millones de miembros". [4]

As a part of their effort to turn Watson into a practical tool, IBM researchers confronted one of the primary tenets of the big data revolution: the idea that prediction based on correlation is sufficient, and that a deep understanding of causation is usually both unachievable and unnecessary. A new feature they named “WatsonPaths” goes beyond simply providing an answer and lets researchers see the specific sources Watson consulted, the logic it used in its evaluation, and the inferences it made on

Deep Blue, IBM’s chess-playing computer, was a sole entity, and not a team of self-improving ASIs, but the feeling of going up against it is instructive. Two grandmasters said the same thing: “It’s like a wall coming at you.” IBM’s Jeopardy! champion, Watson, was a team of AIs—to answer every question it performed this AI force multiplier trick, conducting searches in parallel before assigning a probability to each answer.

En realidad, ya hemos cruzado esta línea en lo que a la medicina se refiere. En el hospital ya no somos individuos. ¿Quién cree el lector que tomará las decisiones más trascendentales sobre su cuerpo y su salud a lo largo de su vida? Es muy probable que muchas de tales decisiones las tomen algoritmos informáticos como el Watson de IBM. Y esto no es necesariamente una mala noticia. Los diabéticos ya llevan sensores que comprueban automáticamente su nivel de azúcar varias veces al día, y les alertan siempre que este cruza un umbral peligroso. En 2014, investigadores de la Universidad de Yale anunciaron la primera prueba exitosa con un «páncreas artificial» controlado por un iPhone. Cincuenta y dos diabéticos participaron en el experimento. Cada paciente tenía un sensor diminuto y una bomba minúscula implantados en el estómago. La bomba estaba conectada a pequeños tubos de insulina y glucagón, dos hormonas que regulan conjuntamente los niveles de azúcar en sangre. El sensor medía constantemente el nivel de azúcar y transmitía los datos a un iPhone. Este contenía una aplicación que analizaba la información y, siempre que era necesario, daba órdenes a la bomba, que inyectaba cantidades determinadas de insulina o de glucagón…, todo ello sin necesidad de intervención humana.[22]

a harbinger of a third wave of computing, one that blurred the line between augmented human intelligence and artificial intelligence. “The first generation of computers were machines that counted and tabulated,” Rometty says, harking back to IBM’s roots in Herman Hollerith’s punch-card tabulators used for the 1890 census. “The second generation involved programmable machines that used the von Neumann architecture. You had to tell them what to do.” Beginning with Ada Lovelace, people wrote algorithms that instructed these computers, step by step, how to perform tasks. “Because of the proliferation of data,” Rometty adds, “there is no choice but to have a third generation, which are systems that are not programmed, they learn.”27 But even as this occurs, the process could remain one of partnership and symbiosis with humans rather than one designed to relegate humans to the dustbin of history. Larry Norton, a breast cancer specialist at New York’s Memorial Sloan-Kettering Cancer Center, was part of the team that worked with Watson. “Computer science is going to evolve rapidly, and medicine will evolve with it,” he said. “This is coevolution. We’ll help each other.”28 This belief that machines and humans will get smarter together is a process that Doug Engelbart called “bootstrapping” and “coevolution.”29 It raises an interesting prospect: perhaps no matter how fast computers progress, artificial intelligence may never outstrip the intelligence of the human-machine partnership. Let us assume, for example, that a machine someday exhibits all of the mental capabilities of a human: giving the outward appearance of recognizing patterns, perceiving emotions, appreciating beauty, creating art, having desires, forming moral values, and pursuing goals. Such a machine might be able to pass a Turing Test. It might even pass what we could call the Ada Test, which is that it could appear to “originate” its own thoughts that go beyond what we humans program it to do. There would, however, be still another hurdle before we could say that artificial intelligence has triumphed over augmented intelligence. We can call it the Licklider Test. It would go beyond asking whether a machine could replicate all the components of human intelligence to ask whether the machine accomplishes these tasks better when whirring away completely on its own or when working in conjunction with humans. In other words, is it possible that humans and machines working in partnership will be indefinitely more powerful than an artificial intelligence machine working alone?

I once heard a story about Tom Watson, the founder of IBM. Asked to what he attributed the phenomenal success of IBM, he is said to have answered: IBM is what it is today for three special reasons. The first reason is that, at the very beginning, I had a very clear picture of what the company would look like when it was finally done. You might say I had a model in my mind of what it would look like when the dream—my vision—was in place. The second reason was that once I had that picture, I then asked myself how a company which looked like that would have to act. I then created a picture of how IBM would act when it was finally done. The third reason IBM has been so successful was that once I had a picture of how IBM would look when the dream was in place and how such a company would have to act, I then realized that, unless we began to act that way from the very beginning, we would never get there. In other words, I realized that for IBM to become a great company it would have to act like a great company long before it ever became one. From the very outset, IBM was fashioned after the template of my vision. And each and every day we attempted to model the company after that template. At the end of each day, we asked ourselves how well we did, discovered the disparity between where we were and where we had committed ourselves to be, and, at the start of the following day, set out to make up for the difference. Every day at IBM was a day devoted to business development, not doing business. We didn’t do business at IBM, we built one Now,

Or as the IBM pioneer Thomas Watson said, “If you want to succeed, double your failure rate.

When I visited Watson and its programmers recently at IBM’s main research facility—where the machine, consisting of a stack of servers, resides alone in a basement, humming quietly and waiting for questions to crunch on—I inquired (directing my queries to the nearby humans, not the machine) whether Watson might ever turn the tables on us and start asking us wickedly complex questions. While that’s not its purpose, its programmers point out something interesting and quite promising: As Watson comes in increasing contact with doctors and medical students currently using the system, the machine is slowly training them to ask more and better questions in order to pull the information they need out of the system. As it trains them to be better questioners, Watson will almost certainly help them to be better doctors.

The proactive approach to a mistake is to acknowledge it instantly, correct and learn from it. This literally turns a failure into a success. “Success,” said IBM founder T. J. Watson, “is on the far side of failure.” But not to acknowledge a mistake, not to correct it and learn from it, is a mistake of a different order. It usually puts a person on a self-deceiving, self-justifying path, often involving rationalization (rational lies) to

The underlying architectures adopted by IBM Watson and AlphaGo have shown super-humans strengths and sub-humans limitations.

Kurzweil cites numerous quotations from prominent people in history who completely underestimated the progress and impact of technology. Here are a few examples. IBM’s chairman, Thomas J. Watson, in 1943: ‘I think there is a world market for maybe five computers.’ Digital Equipment Corporation’s co-founder Ken Olsen in 1977: ‘There’s no reason for individuals to have a computer in their home.’ Bill Gates in 1981: ‘640,000 bytes of memory ought to be enough for anybody.

Mandelbrot appended this statement to his entry in Who’s Who: “Science would be ruined if (like sports) it were to put competition above everything else, and if it were to clarify the rules of competition by withdrawing entirely into narrowly defined specialties. The rare scholars who are nomads-by–choice are essential to the intellectual welfare of the settled disciplines.” This nomad-by–choice, who also called himself a pioneer-by–necessity, withdrew from academe when he withdrew from France, accepting the shelter of IBM’s Thomas J. Watson Research Center.

The proactive approach to a mistake is to acknowledge it instantly, correct and learn from it. This literally turns a failure into a success. “Success,” said IBM founder T. J. Watson, “is on the far side of failure.

Without 'ideas,' the material creations we regard so highly could not have been conceived or produced. … Without 'high ideals' we cannot derive from the marvels of our modern creative and productive genius the greatest good for the greatest number of people. Tom Watson Sr., “Production,” June 1947

That company was International Business Machines, and its chairman was Thomas J. Watson.