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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

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). The match concluded on February 17, 1996.

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.

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. One of my colleagues recently told me that he'd been prescribed a statin for a heart condition. Binary code The word 'Wikipedia' represented in ASCII binary. In computing and telecommunication, binary codes are used for various methods of encoding data, such as character strings, into bit strings. Those methods may use fixed-width or variable-width strings. In a fixed-width binary code, each letter, digit, or other character is represented by a bit string of the same length; that bit string, interpreted as a binary number, is usually displayed in code tables in octal, decimal or hexadecimal notation.

Machine learning Machine learning is the subfield of computer science that gives computers the ability to learn without being explicitly programmed (Arthur Samuel, 1959).[1] Evolved from the study of pattern recognition and computational learning theory in artificial intelligence,[2] machine learning explores the study and construction of algorithms that can learn from and make predictions on data[3] – such algorithms overcome following strictly static program instructions by making data driven predictions or decisions,[4]:2 through building a model from sample inputs. Machine learning is employed in a range of computing tasks where designing and programming explicit algorithms is infeasible; example applications include spam filtering, detection of network intruders or malicious insiders working towards a data breach,[5] optical character recognition (OCR),[6] search engines and computer vision. Overview[edit] Tom M. Types of problems and tasks[edit]

Rise of Neurocinema: How Hollywood Studios Harness Your Brainwaves to Win Oscars One thing you aren't likely to hear Sunday night from the Oscar-winning producer after accepting the trophy for Best Picture: "I'd like to thank my neuroscience partners who helped us enhance the film's script, characters, and scenes." It's not that far-fetched, though. A sizable number of neuromarketing companies already brain test movie trailers for the major studios through fMRI, EEG, galvanic skin response, eye-tracking and other biometric approaches. For now, the test data helps the studios and distributors better market the movie. But what about using brain feedback to help make the movie? A trailblazing few firms and studios have delved into the upstart practice of "neurocinema," the method of using neurofeedback to help moviemakers vet and refine film elements such as scripts, characters, plots, scenes, and effects.

Doctors Diagnose in a Jiffy—and Using Common Regions of the Brain A new view on medicine: How doctors view x-rays; courtesy of Melo M, Scarpin DJ, Amaro E Jr, Passos RBD, Sato JR, et al. Medical school might be a long, slow slog, but once doctors have their training, they can often make diagnoses in a matter of moments. New research suggests that doctors actually identify an abnormality in less than two seconds—not much longer than it takes them to name an animal or a letter of the alphabet. Twenty-five radiologists submitted to having their brains scanned while performing visual diagnoses of chest x-rays. Mixed in with images of abnormal chest x-rays were clean ones on which the outline of an animal or consonant had been superimposed to test the speed with which doctors recognized familiar objects. The researchers, led by Marcio Melo, of the Laboratory of Medical Informatics at the University of São Paulo, found that the same regions of the brain were active when doctors correctly identified any of the three objects.

What Is Binary? - mobile wiseGEEK Binary Code is most commonly used in BIOS(Basic Input Output System).Which is used in computers Binary numbers (1 or 0) represent on(1) or off(0). Typically you work out binary like this: Yes, Computers Can Think NEW HAVEN— Last year, after Garry Kasparov's chess victory over the I.B.M. computer Deep Blue, I told the students in my Introduction to Artificial Intelligence class that it would be many years before computers could challenge the best humans. Now that I and many others have been proved wrong, a lot of people have been rushing to assure us that Deep Blue is not actually intelligent and that this victory has no bearing on the future of artificial intelligence. Although I agree that the computer is not very intelligent, to say that it shows no intelligence at all demonstrates a basic misunderstanding of what it does and of the goals and methods of artificial intelligence research. True, Deep Blue is very narrow. It can win a chess game, but it can't recognize, much less pick up, a chess piece. It can't even carry on a conversation about the game it just won.