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Watson (computer)

Watson (computer)
Watson is an artificially intelligent computer system capable of answering questions posed in natural language,[2] developed in IBM's DeepQA project by a research team led by principal investigator David Ferrucci. Watson was named after IBM's first CEO and industrialist Thomas J. Watson.[3][4] The computer system was specifically developed to answer questions on the quiz show Jeopardy![5] In 2011, Watson competed on Jeopardy! against former winners Brad Rutter and Ken Jennings.[3][6] Watson received the first place prize of $1 million.[7] Watson had access to 200 million pages of structured and unstructured content consuming four terabytes of disk storage[8] including the full text of Wikipedia,[9] but was not connected to the Internet during the game.[10][11] For each clue, Watson's three most probable responses were displayed on the television screen. The high-level architecture of IBM's DeepQA used in Watson[14] When playing Jeopardy! The Jeopardy! Related:  Machine Learning

Computers, A.I. SimulConsult New qubit control bodes well for future of quantum computing (—Yale University scientists have found a way to observe quantum information while preserving its integrity, an achievement that offers researchers greater control in the volatile realm of quantum mechanics and greatly improves the prospects of quantum computing. Quantum computers would be exponentially faster than the most powerful computers of today. "Our experiment is a dress rehearsal for a type of process essential for quantum computing," said Michel Devoret, the Frederick William Beinecke Professor of Applied Physics & Physics at Yale and principal investigator of research published Jan. 11 in the journal Science. "What this experiment really allows is an active understanding of quantum mechanics. It's one thing to stare at a theoretical formula and it's another thing to be able to control a real quantum object." In quantum systems, microscopic units called qubits represent information. "As long as you know what error process has occurred, you can correct," Devoret said.

Deep Blue (chess computer) Deep Blue After Deep Thought's 1989 match against Kasparov, IBM held a contest to rename the chess machine and it became "Deep Blue", a play on IBM's nickname, "Big Blue".[8] After a scaled down version of Deep Blue, Deep Blue Jr., played Grandmaster Joel Benjamin, Hsu and Campbell decided that Benjamin was the expert they were looking for to develop Deep Blue's opening book, and Benjamin was signed by IBM Research to assist with the preparations for Deep Blue's matches against Garry Kasparov.[9] On February 10, 1996, Deep Blue became the first machine to win a chess game against a reigning world champion (Garry Kasparov) under regular time controls. However, Kasparov won three and drew two of the following five games, beating Deep Blue by a score of 4–2 (wins count 1 point, draws count ½ point). Deep Blue was then heavily upgraded (unofficially nicknamed "Deeper Blue")[11] and played Kasparov again in May 1997, winning the six-game rematch 3½–2½, ending on May 11. Notes Bibliography

Peter Joseph responde: El activismo ético dentro del sistema no es la solución. ! Introduction to NeuroAesthetics ! Is Your Clinical Database Up to Speed? - Healthcare - Clinical Information Systems If the scientific data going into these repositories is flawed, so are your clinicians' decisions. Garbage in, garbage out is a common expression that's especially relevant to health IT. The quality of the data that goes into an e-prescribing program or clinical decision support system determines the accuracy of the diagnoses and treatment decisions coming out of your doctors and nurses. Two examples illustrate the need to keep the GIGO mantra front of mind. My previous experience as a nutritionist working with cardiac patients, plus conversations with several friends who have used statins, suggest otherwise. [For more background on e-prescribing tools, see 6 E-Prescribing Vendors To Watch.] So chances are the e-prescribing tools your clinicians use are telling them not to be too concerned. Why the discrepancy? Think for a moment about how these divergent stats can affect clinical outcomes. When are emerging technologies ready for clinical use? More Insights

Quantum mystery of light revealed - Technology & science - Science - LiveScience Is light made of waves, or particles? This fundamental question has dogged scientists for decades, because light seems to be both. However, until now, experiments have revealed light to act either like a particle, or a wave, but never the two at once. Now, for the first time, a new type of experiment has shown light behaving like both a particle and a wave simultaneously, providing a new dimension to the quandary that could help reveal the true nature of light, and of the whole quantum world. The debate goes back at least as far as Isaac Newton, who advocated that light was made of particles, and James Clerk Maxwell, whose successful theory of electromagnetism, unifying the forces of electricity and magnetism into one, relied on a model of light as a wave. Ultimately, there's good reason to think that light is both a particle and a wave. Depending on which type of experiment is used, light, or any other type of particle, will behave like a particle or like a wave.

Machine learning Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to effectively perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.[1][2]:2 Machine learning algorithms are used in a wide variety of applications, such as email filtering, and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. Overview[edit] The name machine learning was coined in 1959 by Arthur Samuel.[5] Tom M. Machine learning tasks[edit] History and relationships to other fields[edit] Theory[edit]

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