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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. 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. group mckeeena ! Introduction to NeuroAesthetics ! Machine learning Machine learning (ML) is the study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of 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 the applications of email filtering, detection of network intruders, 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. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. 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]

Science & Environment - Why everyone must understand science Many people feel excluded by science, but philosopher AC Grayling says this makes us slaves to technology. The less we know the more likely we are to be manipulated by others. Science is undoubtedly humanity’s greatest achievement, says AC Grayling, Master of the New College of the Humanities . People have to wake up to the fact that they have to be part of the story in thinking about science, and thinking about the meaning of science as it applies to our world. People feel excluded by science and debates about science, they use laptops, they fly in planes, use appliances in the home and they don’t know what’s behind this technology. People are aware that there are lots of problems with the environment and the climate. We have to start this at school. We have to have a healthy scepticism, says Grayling, people can’t just shut their eyes to things that are important.

Watson Articles and papers on neuroesthetics The Neural Sources of Salvador Dali's Ambiguity S Zeki Coming soon ! The neural correlates of beauty S Zeki and H Kawabata Journal of Neurophysiology (J Neurophysiol 91: 1699-1705, 2004) PDF Cerveau & Psycho Special edition of Pour la Science nº 2 juin-août 2003 This contains several articles of interest to neuroesthetics Cervello pittore L Ticini Stile e Arte Maggio (2003) Page 1 2 Hearing colors, tasting shapes V S Ramachandran and E M Hubbard Scientific American May (2003) Website La creatività artistica e il cervello L Ticini Arte & Cultura Marzo (2003) PDF Trying to make sense of art S Zeki Nature 418:918-919 29 August (2002) PDF Neural concept formation and art: Dante, Michelangelo, Wagner S Zeki Journal of Consciousness Studies 9, 53-76 (2002) PDF Artistic creativity and the brain S Zeki Science 293, 51-52 (2001) PDF The science of art.