2012年6月30日 星期六

小說《鏡花緣》中的科技史




李汝珍《鏡花緣》的引論(胡適):全篇為女子爭平等的/ 十九世紀初期科技在一般人士的普及 李汝珍《鏡花緣》的引論(胡適):全篇為女子爭平等的/ 十九世紀初期科技在一般人士的普及




《鏡花緣》(李汝珍)此書雖有“掉書袋”的毛病,但全篇為女子爭平等的待遇,確是一部很難得的書。亞東圖書館本。---胡適



胡適或許會喜讀這類的文章:
《從《鏡花緣》試探十九世紀初期科技在一般人士的普及(1991《中國科技史專刊》台北) ,參考: 何丙郁《學思歷程的回憶:科學、人文、李約瑟》 192

「猜拳機械人」

日本東京大學研究室,發明了一架「猜拳機械人」,和真人猜拳百戰百勝,難道人工智慧的技術已經如此成熟,電腦終於可以完全預測人類的下一步舉動?

其實,關鍵在於這部機械人配備鏡頭,表面上看起來與人類同時出拳,但實際是利用不到千分之二秒的時間,分析人類的手指動作,見人出布就出剪刀,人出剪刀就出石頭,人出石頭就出布,就算人類中途「偷吃步」,電腦也分辨得出來。

研究人員希望提升動作辨識技術,以令機械人可即時配合人類動作,提供支援。

2012年6月29日 星期五

夏商周斷代方法



    上個世紀中國指定一些學者進行夏商周斷代史工程時,何炳棣先生就極度關心。因為這個題目不僅僅是中國學者所要解決的,也屬於世界史範圍的大題目。中國屬於世界四大文明古國,但是談起五千年的歷史,似乎終覺有點氣短,因為傳說史終究無法作為有文字、實物記載的歷史來介紹。跟其他文明古國相比較,也沒有輝煌的雕塑、建築等實物。但是我們不能用簡單的民族情結來代替嚴肅的科學研究。 “走出疑古”如果不嚴謹就會走出科學。


何炳棣李學勤面對面辯論夏商周斷代方法


記者計亞男、莊建http://www.gmw.cn 2010-05-28



本報北京5月27日電掌聲中,93歲的何炳棣教授走向講台。繼5月13日在清華高等研究院演講之後,今天再次在清華大學歷史係做題為“夏商周斷代的方法問題”的講演。今天下午,在清華大學歷史講壇,何炳棣、劉雨和李學勤三位知名學者互相交流對話夏商周斷代工程的方法問題。 


不同以往在書信中的多次辯學,何炳棣終於面對李學勤說出了自己心中塊壘。 “我與李先生的根本不同,是在方法上,原則上,史德上。李先生學術上成就非常之高,原則非常之強。我們辯論的焦點,還是公元前1027年,還是武王伐紂。”
“夏商周斷代工程”(以下簡稱“斷代工程”)在第九個五年計劃中,指定為重點科技攻關項目之一,從全國20多個科研、文博、高教等單位徵選歷史、考古、天文、古文字、自然科技史、核物理等方面200多位專家學者進行多學科分組而又交叉的研究。
何炳棣指出,“斷代工程”在頭四年聯合攻關期間,對有關中國古史年代​​的文獻、古器物、天文等資料已全部蒐集和初步檢討,事實上對過去種種主觀偏頗的說法已經做了大規模的淘汰工作。例如古今中外爭辯最激烈的武王克商之年,本有代表四十四種不同意見的百篇以上的論說,目前只剩下《古本竹書紀年》的公元前1027年和夏商周斷代工程所“選擇”的公元前1046年的大“決賽”了。此外,更重要的是近二三十年來海外和國內已有不少對中國古代天文記錄較準確的推算,足資核證《古本竹書紀年》及其他文獻中所保存的若干重要歷史年代之是否正確。
李學勤認為:“夏商周斷代工程現在只是階段性成果。從1996年到2000年,有標題的會議就開了50多次,為金文歷譜開的會已經數不清了。對於年代學這樣重大的問題,馬上有一個共同的結論是不可能的。科學是不斷前進的,有時可能倒退,遇到曲折,我們希望工作沒有白做,不是馬上得到一個結論,而是越來越接近真理。”
“任何學術都包含兩個元素:一個是知識層面的,一個是倫理元素。學習不僅僅是在知識層面學習,更要學習形而上的東西。同學們要學習他們這兩個方面。三位先生研究論辯的示範就包括這兩個層面。他們很誠懇,充分展示了學者的知識和品格。”講演主持人、清華大學歷史系教授劉桂生這樣評價。


 ***

最新ニュース

世界最古、2万年前の土器 中国で発見、料理に使う?
 中国江西省の洞窟から見つかっていた土器のかけらが、土器としては世界最古の2万年前のものであることを……… (19:57)[記事へ]
中国・神舟9号が帰還 初の有人ドッキングに成功
 中国初の女性宇宙飛行士、劉洋さんら3人を乗せた有人宇宙船「神舟9号」が29日午前10時(日本時間同……… (12:22)[記事へ]
脳動脈瘤、7ミリ以上は破裂リスク増大 学会が発表購読者は全文読めます
 くも膜下出血につながる「脳動脈瘤(りゅう)」は、7ミリ以上になると破裂のリスクが高まり、7~9ミリ……… (15:44)[記事へ]

2012年6月19日 星期二

supercomputers ranking....What Is I.B.M.’s Watson?

 
 

 スパコン「京」世界一から転落 The K computer/Abnormal patterns on...

IBM's Watson: 1. Human Jeopardy Contestants: 0.
PC Magazine
New video from Engadget shows IBM's Watson question-answer supercomputer putting the hurt on Jeopardy champions Ken Jennings (74 wins in a row, ...
 
Smarter Than You Think

What Is I.B.M.’s Watson?


Danielle Levitt for The New York Times
A part of Watson’s ‘‘brain,’’ located in a room near the mock ‘‘Jeopardy!’’ set.




“Toured the Burj in this U.A.E. city. They say it’s the tallest tower in the world; looked over the ledge and lost my lunch.”

Smarter Than You Think

Articles in this series, appearing in The New York Times in the coming months, will examine the recent advances in artificial intelligence and robotics and their potential impact on society.
Danielle Levitt for The New York Times
MASTERMIND The one behind Watson’s mastermind, that is: David Ferrucci of I.B.M., who himself is not a huge “Jeopardy!” fan.
Danielle Levitt for The New York Times
THE ‘‘FANS’’ I.B.M. employees (including Ferrucci, front row, far right) who worked on the project, during one of the test “Jeopardy!” matches in January.
Danielle Levitt for The New York Times

Readers' Comments

Readers shared their thoughts on this article.
This is the quintessential sort of clue you hear on the TV game show “Jeopardy!” It’s witty (the clue’s category is “Postcards From the Edge”), demands a large store of trivia and requires contestants to make confident, split-second decisions. This particular clue appeared in a mock version of the game in December, held in Hawthorne, N.Y. at one of I.B.M.’s research labs. Two contestants — Dorothy Gilmartin, a health teacher with her hair tied back in a ponytail, and Alison Kolani, a copy editor — furrowed their brows in concentration. Who would be the first to answer?
Neither, as it turned out. Both were beaten to the buzzer by the third combatant: Watson, a supercomputer.
For the last three years, I.B.M. scientists have been developing what they expect will be the world’s most advanced “question answering” machine, able to understand a question posed in everyday human elocution — “natural language,” as computer scientists call it — and respond with a precise, factual answer. In other words, it must do more than what search engines like Google and Bing do, which is merely point to a document where you might find the answer. It has to pluck out the correct answer itself. Technologists have long regarded this sort of artificial intelligence as a holy grail, because it would allow machines to converse more naturally with people, letting us ask questions instead of typing keywords. Software firms and university scientists have produced question-answering systems for years, but these have mostly been limited to simply phrased questions. Nobody ever tackled “Jeopardy!” because experts assumed that even for the latest artificial intelligence, the game was simply too hard: the clues are too puzzling and allusive, and the breadth of trivia is too wide.
With Watson, I.B.M. claims it has cracked the problem — and aims to prove as much on national TV. The producers of “Jeopardy!” have agreed to pit Watson against some of the game’s best former players as early as this fall. To test Watson’s capabilities against actual humans, I.B.M.’s scientists began holding live matches last winter. They mocked up a conference room to resemble the actual “Jeopardy!” set, including buzzers and stations for the human contestants, brought in former contestants from the show and even hired a host for the occasion: Todd Alan Crain, who plays a newscaster on the satirical Onion News Network.
Technically speaking, Watson wasn’t in the room. It was one floor up and consisted of a roomful of servers working at speeds thousands of times faster than most ordinary desktops. Over its three-year life, Watson stored the content of tens of millions of documents, which it now accessed to answer questions about almost anything. (Watson is not connected to the Internet; like all “Jeopardy!” competitors, it knows only what is already in its “brain.”) During the sparring matches, Watson received the questions as electronic texts at the same moment they were made visible to the human players; to answer a question, Watson spoke in a machine-synthesized voice through a small black speaker on the game-show set. When it answered the Burj clue — “What is Dubai?” (“Jeopardy!” answers must be phrased as questions) — it sounded like a perkier cousin of the computer in the movie “WarGames” that nearly destroyed the world by trying to start a nuclear war.
This time, though, the computer was doing the right thing. Watson won $1,000 (in pretend money, anyway), pulled ahead and eventually defeated Gilmartin and Kolani soundly, winning $18,400 to their $12,000 each.
“Watson,” Crain shouted, “is our new champion!”
It was just the beginning. Over the rest of the day, Watson went on a tear, winning four of six games. It displayed remarkable facility with cultural trivia (“This action flick starring Roy Scheider in a high-tech police helicopter was also briefly a TV series” — “What is ‘Blue Thunder’?”), science (“The greyhound originated more than 5,000 years ago in this African country, where it was used to hunt gazelles” — “What is Egypt?”) and sophisticated wordplay (“Classic candy bar that’s a female Supreme Court justice” — “What is Baby Ruth Ginsburg?”).
By the end of the day, the seven human contestants were impressed, and even slightly unnerved, by Watson. Several made references to Skynet, the computer system in the “Terminator” movies that achieves consciousness and decides humanity should be destroyed. “My husband and I talked about what my role in this was,” Samantha Boardman, a graduate student, told me jokingly. “Was I the thing that was going to help the A.I. become aware of itself?” She had distinguished herself with her swift responses to the “Rhyme Time” puzzles in one of her games, winning nearly all of them before Watson could figure out the clues, but it didn’t help. The computer still beat her three times. In one game, she finished with no money.
“He plays to win,” Boardman said, shaking her head. “He’s really not messing around!” Like most of the contestants, she had started calling Watson “he.”
WE LIVE IN AN AGE of increasingly smart machines. In recent years, engineers have pushed into areas, from voice recognition to robotics to search engines, that once seemed to be the preserve of humans. But I.B.M. has a particular knack for pitting man against machine. In 1997, the company’s supercomputer Deep Blue famously beat the grandmaster Garry Kasparov at chess, a feat that generated enormous publicity for I.B.M. It did not, however, produce a marketable product; the technical accomplishment — playing chess really well — didnt translate to real-world business problems and so produced little direct profit for I.B.M. In the mid ’00s, the company’s top executives were looking for another high-profile project that would provide a similar flood of global publicity. But this time, they wanted a “grand challenge” (as they call it internally), that would meet a real-world need.
Question-answering seemed to be a good fit. In the last decade, question-answering systems have become increasingly important for firms dealing with mountains of documents. Legal firms, for example, need to quickly sift through case law to find a useful precedent or citation; help-desk workers often have to negotiate enormous databases of product information to find an answer for an agitated customer on the line. In situations like these, speed can often be of the essence; in the case of help desks, labor is billed by the minute, so high-tech firms with slender margins often lose their profits providing telephone support. How could I.B.M. push question-answering technology further?
When one I.B.M. executive suggested taking on “Jeopardy!” he was immediately pooh-poohed. Deep Blue was able to play chess well because the game is perfectly logical, with fairly simple rules; it can be reduced easily to math, which computers handle superbly. But the rules of language are much trickier. At the time, the very best question-answering systems — some created by software firms, some by university researchers — could sort through news articles on their own and answer questions about the content, but they understood only questions stated in very simple language (“What is the capital of Russia?”); in government-run competitions, the top systems answered correctly only about 70 percent of the time, and many were far worse. “Jeopardy!” with its witty, punning questions, seemed beyond their capabilities. What’s more, winning on “Jeopardy!” requires finding an answer in a few seconds. The top question-answering machines often spent longer, even entire minutes, doing the same thing.
“The reaction was basically, ‘No, it’s too hard, forget it, no way can you do it,’ ” David Ferrucci told me not long ago. Ferrucci, I.B.M.’s senior manager for its Semantic Analysis and Integration department, heads the Watson project, and I met him for the first time last November at I.B.M.’s lab. An artificial-intelligence researcher who has long specialized in question-answering systems, Ferrucci chafed at the slow progress in the field. A fixture in the office in the evenings and on weekends, he is witty, voluble and intense. While dining out recently, his wife asked the waiter if Ferrucci’s meal included any dairy. “Is he lactose intolerant?” the waiter inquired. “Yes,” his wife replied, “and just generally intolerable.” Ferrucci told me he was recently prescribed a mouth guard because the stress of watching Watson play had him clenching his teeth excessively.
Ferrucci was never an aficionado of “Jeopardy!” (“I’ve certainly seen it,” he said with a shrug. “I’m not a big fan.”) But he craved an ambitious goal that would impel him to break new ground, that would verge on science fiction, and this fit the bill. “The computer on ‘Star Trek’ is a question-answering machine,” he says. “It understands what you’re asking and provides just the right chunk of response that you needed. When is the computer going to get to a point where the computer knows how to talk to you? That’s my question.”
What makes language so hard for computers, Ferrucci explained, is that it’s full of “intended meaning.” When people decode what someone else is saying, we can easily unpack the many nuanced allusions and connotations in every sentence. He gave me an example in the form of a “Jeopardy!” clue: “The name of this hat is elementary, my dear contestant.” People readily detect the wordplay here — the echo of “elementary, my dear Watson,” the famous phrase associated with Sherlock Holmes — and immediately recall that the Hollywood version of Holmes sports a deerstalker hat. But for a computer, there is no simple way to identify “elementary, my dear contestant” as wordplay. Cleverly matching different keywords, and even different fragments of the sentence — which in part is how most search engines work these days — isn’t enough, either. (Type that clue into Google, and you’ll get first-page referrals to “elementary, my dear watson” but none to deerstalker hats.)
What’s more, even if a computer determines that the actual underlying question is “What sort of hat does Sherlock Holmes wear?” its data may not be stored in such a way that enables it to extract a precise answer. For years, computer scientists built question-answering systems by creating specialized databases, in which certain facts about the world were recorded and linked together. You could do this with Sherlock Holmes by building a database that includes connections between catchphrases and his hat and his violin-playing. But that database would be pretty narrow; it wouldn’t be able to answer questions about nuclear power, or fish species, or the history of France. Those would require their own hand-made databases. Pretty soon you’d face the impossible task of organizing all the information known to man — of “boiling the ocean,” as Ferrucci put it. In computer science, this is known as a “bottleneck” problem. And even if you could get past it, you might then face the issue of “brittleness”: if your database contains only facts you input manually, it breaks any time you ask it a question about something beyond that material. There’s no way to hand-write a database that would include the answer to every “Jeopardy!” clue, because the subject matter is potentially all human knowledge.
The great shift in artificial intelligence began in the last 10 years, when computer scientists began using statistics to analyze huge piles of documents, like books and news stories. They wrote algorithms that could take any subject and automatically learn what types of words are, statistically speaking, most (and least) associated with it. Using this method, you could put hundreds of articles and books and movie reviews discussing Sherlock Holmes into the computer, and it would calculate that the words “deerstalker hat” and “Professor Moriarty” and “opium” are frequently correlated with one another, but not with, say, the Super Bowl. So at that point you could present the computer with a question that didn’t mention Sherlock Holmes by name, but if the machine detected certain associated words, it could conclude that Holmes was the probable subject — and it could also identify hundreds of other concepts and words that weren’t present but that were likely to be related to Holmes, like “Baker Street” and “chemistry.”
In theory, this sort of statistical computation has been possible for decades, but it was impractical. Computers weren’t fast enough, memory wasn’t expansive enough and in any case there was no easy way to put millions of documents into a computer. All that changed in the early ’00s. Computer power became drastically cheaper, and the amount of online text exploded as millions of people wrote blogs and wikis about anything and everything; news organizations and academic journals also began putting all their works in digital format. What’s more, question-answering experts spent the previous couple of decades creating several linguistic tools that helped computers puzzle through language — like rhyming dictionaries, bulky synonym finders and “classifiers” that recognized the parts of speech.
Still, the era’s best question-answering systems remained nowhere near being able to take on “Jeopardy!” In 2006, Ferrucci tested I.B.M.’s most advanced system — it wasn’t the best in its field but near the top — by giving it 500 questions from previous shows. The results were dismal. He showed me a chart, prepared by I.B.M., of how real-life “Jeopardy!” champions perform on the TV show. They are clustered at the top in what Ferrucci calls “the winner’s cloud,” which consists of individuals who are the first to hit the buzzer about 50 percent of the time and, after having “won” the buzz, solve on average 85 to 95 percent of the clues. In contrast, the I.B.M. system languished at the bottom of the chart. It was rarely confident enough to answer a question, and when it was, it got the right answer only 15 percent of the time. Humans were fast and smart; I.B.M.’s machine was slow and dumb.
“Humans are just — boom! — they’re just plowing through this in just seconds,” Ferrucci said excitedly. “They’re getting the questions, they’re breaking them down, they’re interpreting them, they’re getting the right interpretation, they’re looking this up in their memory, they’re scoring, they’re doing all this just instantly.”
But Ferrucci argued that I.B.M. could be the one to finally play “Jeopardy!” If the firm focused its computer firepower — including its new “BlueGene” servers — on the challenge, Ferrucci could conduct experiments dozens of times faster than anyone had before, allowing him to feed more information into Watson and test new algorithms more quickly. Ferrucci was ambitious for personal reasons too: if he didn’t try this, another computer scientist might — “and then bang, you are irrelevant,” he told me.
“I had no interest spending the next five years of my life pursuing things in the small,” he said. “I wanted to push the limits.” If they could succeed at “Jeopardy!” soon after that they could bring the underlying technology to market as customizable question-answering systems. In 2007, his bosses gave him three to five years and increased his team to 15 people.
FERRUCCI’S MAIN breakthrough was not the design of any single, brilliant new technique for analyzing language. Indeed, many of the statistical techniques Watson employs were already well known by computer scientists. One important thing that makes Watson so different is its enormous speed and memory. Taking advantage of I.B.M.’s supercomputing heft, Ferrucci’s team input millions of documents into Watson to build up its knowledge base — including, he says, “books, reference material, any sort of dictionary, thesauri, folksonomies, taxonomies, encyclopedias, any kind of reference material you can imagine getting your hands on or licensing. Novels, bibles, plays.”
Watson’s speed allows it to try thousands of ways of simultaneously tackling a “Jeopardy!” clue. Most question-answering systems rely on a handful of algorithms, but Ferrucci decided this was why those systems do not work very well: no single algorithm can simulate the human ability to parse language and facts. Instead, Watson uses more than a hundred algorithms at the same time to analyze a question in different ways, generating hundreds of possible solutions. Another set of algorithms ranks these answers according to plausibility; for example, if dozens of algorithms working in different directions all arrive at the same answer, it’s more likely to be the right one. In essence, Watson thinks in probabilities. It produces not one single “right” answer, but an enormous number of possibilities, then ranks them by assessing how likely each one is to answer the question.
Ferrucci showed me how Watson handled this sample “Jeopardy!” clue: “He was presidentially pardoned on Sept. 8, 1974.” In the first pass, the algorithms came up with “Nixon.” To evaluate whether “Nixon” was the best response, Watson performed a clever trick: it inserted the answer into the original phrase — “Nixon was presidentially pardoned on Sept. 8, 1974” — and then ran it as a new search, to see if it also produced results that supported “Nixon” as the right answer. (It did. The new search returned the result “Ford pardoned Nixon on Sept. 8, 1974,” a phrasing so similar to the original clue that it helped make “Nixon” the top-ranked solution.)
Other times, Watson uses algorithms that can perform basic cross-checks against time or space to help detect which answer seems better. When the computer analyzed the clue “In 1594 he took a job as a tax collector in Andalusia,” the two most likely answers generated were “Thoreau” and “Cervantes.” Watson assessed “Thoreau” and discovered his birth year was 1817, at which point the computer ruled him out, because he wasn’t alive in 1594. “Cervantes” became the top-ranked choice.
When Watson is playing a game, Ferrucci lets the audience peek into the computer’s analysis. A monitor shows Watson’s top five answers to a question, with a bar graph beside each indicating its confidence. During one of my visits, the host read the clue “Thousands of prisoners in the Philippines re-enacted the moves of the video of this Michael Jackson hit.” On the monitor, I could see that Watson’s top pick was “Thriller,” with a confidence level of roughly 80 percent. This answer was correct, and Watson buzzed first, so it won $800. Watson’s next four choices — “Music video,” “Billie Jean,” “Smooth Criminal” and “MTV” — had only slivers for their bar graphs. It was a fascinating glimpse into the machine’s workings, because you could spy the connective thread running between the possibilities, even the wrong ones. “Billie Jean” and “Smooth Criminal” were also major hits by Michael Jackson, and “MTV” was the main venue for his videos. But it’s very likely that none of those correlated well with “Philippines.”
After a year, Watson’s performance had moved halfway up to the “winner’s cloud.” By 2008, it had edged into the cloud; on paper, anyway, it could beat some of the lesser “Jeopardy!” champions. Confident they could actually compete on TV, I.B.M. executives called up Harry Friedman, the executive producer of “Jeopardy!” and raised the possibility of putting Watson on the air.
Friedman told me he and his fellow executives were surprised: nobody had ever suggested anything like this. But they quickly accepted the challenge. “Because it’s I.B.M., we took it seriously,” Friedman said. “They had the experience with Deep Blue and the chess match that became legendary.”
WHEN THEY FIRST showed up to play Watson, many of the contestants worried that they didn’t stand a chance. Human memory is frail. In a high-stakes game like “Jeopardy!” players can panic, becoming unable to recall facts they would otherwise remember without difficulty. Watson doesn’t have this problem. It might have trouble with its analysis or be unable to logically connect a relevant piece of text to a question. But it doesn’t forget things. Plus, it has lightning-fast reactions — wouldn’t it simply beat the humans to the buzzer every time?
“We’re relying on nerves — old nerves,” Dorothy Gilmartin complained, halfway through her first game, when it seemed that Watson was winning almost every buzz.
Yet the truth is, in more than 20 games I witnessed between Watson and former “Jeopardy!” players, humans frequently beat Watson to the buzzer. Their advantage lay in the way the game is set up. On “Jeopardy!” when a new clue is given, it pops up on screen visible to all. (Watson gets the text electronically at the same moment.) But contestants are not allowed to hit the buzzer until the host is finished reading the question aloud; on average, it takes the host about six or seven seconds to read the clue.
Players use this precious interval to figure out whether or not they have enough confidence in their answers to hazard hitting the buzzer. After all, buzzing carries a risk: someone who wins the buzz on a $1,000 question but answers it incorrectly loses $1,000.
Often those six or seven seconds weren’t enough time for Watson. The humans reacted more quickly. For example, in one game an $800 clue was “In Poland, pick up some kalafjor if you crave this broccoli relative.” A human contestant jumped on the buzzer as soon as he could. Watson, meanwhile, was still processing. Its top five answers hadn’t appeared on the screen yet. When these finally came up, I could see why it took so long. Something about the question had confused the computer, and its answers came with mere slivers of confidence. The top two were “vegetable” and “cabbage”; the correct answer — “cauliflower” — was the third guess.
To avoid losing money — Watson doesn’t care about the money, obviously; winnings are simply a way for I.B.M. to see how fast and accurately its system is performing — Ferrucci’s team has programmed Watson generally not to buzz until it arrives at an answer with a high confidence level. In this regard, Watson is actually at a disadvantage, because the best “Jeopardy!” players regularly hit the buzzer as soon as it’s possible to do so, even if it’s before they’ve figured out the clue. “Jeopardy!” rules give them five seconds to answer after winning the buzz. So long as they have a good feeling in their gut, they’ll pounce on the buzzer, trusting that in those few extra seconds the answer will pop into their heads. Ferrucci told me that the best human contestants he had brought in to play against Watson were amazingly fast. “They can buzz in 10 milliseconds,” he said, sounding astonished. “Zero milliseconds!”
On the third day I watched Watson play, it did quite poorly, losing four of seven games, in one case without any winnings at all. Often Watson appeared to misunderstand the clue and offered answers so inexplicable that the audience erupted in laughter. Faced with the clue “This ‘insect’ of a gangster was a real-life hit man for Murder Incorporated in the 1930s & ’40s,” Watson responded with “James Cagney.” Up on the screen, I could see that none of its lesser choices were the correct one, “Bugsy Siegel.” Later, when asked to complete the phrase “Toto, I’ve a feeling we’re not in Ka—,” Watson offered “not in Kansas anymore,” which was incorrect, since the precise phrasing was simply “Kansas anymore,” and “Jeopardy!” is strict about phrasings. When I looked at the screen, I noticed that the answers Watson had ranked lower were pretty odd, including “Steve Porcaro,” the keyboardist for the band Toto (which made a vague sort of sense), and “Jackie Chan” (which really didn’t). In another game, Watson’s logic appeared to fall down some odd semantic rabbit hole, repeatedly giving the answer “Tommy Lee Jones” — the name of the Hollywood actor — to several clues that had nothing to do with him.
In the corner of the conference room, Ferrucci sat typing into a laptop. Whenever Watson got a question wrong, Ferrucci winced and stamped his feet in frustration, like a college-football coach watching dropped passes. “This is torture,” he added, laughing.
Seeing Watson’s errors, you can sometimes get a sense of its cognitive shortcomings. For example, in “Jeopardy!” the category heading often includes a bit of wordplay that explains how the clues are to be addressed. Watson sometimes appeared to mistakenly analyze the entire category and thus botch every clue in it. One game included the category “Stately Botanical Gardens,” which indicated that every clue would list several gardens, and the answer was the relevant state. Watson clearly didn’t grasp this; it answered “botanic garden” repeatedly. I also noticed that when Watson was faced with very short clues — ones with only a word or two — it often seemed to lose the race to the buzzer, possibly because the host read the clues so quickly that Watson didn’t have enough time to do its full calculations. The humans, in contrast, simply trusted their guts and jumped.
Ferrucci refused to talk on the record about Watson’s blind spots. He’s aware of them; indeed, his team does “error analysis” after each game, tracing how and why Watson messed up. But he is terrified that if competitors knew what types of questions Watson was bad at, they could prepare by boning up in specific areas. I.B.M. required all its sparring-match contestants to sign nondisclosure agreements prohibiting them from discussing their own observations on what, precisely, Watson was good and bad at. I signed no such agreement, so I was free to describe what I saw; but Ferrucci wasn’t about to make it easier for me by cataloguing Watson’s vulnerabilities.
Computer scientists I spoke to agreed that witty, allusive clues will probably be Watson’s weak point. “Retrieval of obscure Italian poets is easy — [Watson] will never forget that one,” Peter Norvig, the director of research at Google, told me. “But ‘Jeopardy!’ tends to have a lot of wordplay, and that’s going to be a challenge.” Certainly on many occasions this seemed to be true. Still, at other times I was startled by Watson’s eerily humanlike ability to untangle astonishingly coy clues. During one game, a category was “All-Eddie Before & After,” indicating that the clue would hint at two different things that need to be blended together, one of which included the name “Eddie.” The $2,000 clue was “A ‘Green Acres’ star goes existential (& French) as the author of ‘The Fall.’ ” Watson nailed it perfectly: “Who is Eddie Albert Camus?”
Ultimately, Watson’s greatest edge at “Jeopardy!” probably isn’t its perfect memory or lightning speed. It is the computer’s lack of emotion. “Managing your emotions is an enormous part of doing well” on “Jeopardy!” Bob Harris, a five-time champion, told me. “Every single time I’ve ever missed a Daily Double, I always miss the next clue, because I’m still kicking myself.” Because there is only a short period before the next clue comes along, the stress can carry over. Similarly, humans can become much more intimidated by a $2,000 clue than a $200 one, because the more expensive clues are presumably written to be much harder.
Whether Watson will win when it goes on TV in a real “Jeopardy!” match depends on whom “Jeopardy!” pits against the computer. Watson will not appear as a contestant on the regular show; instead, “Jeopardy!” will hold a special match pitting Watson against one or more famous winners from the past. If the contest includes Ken Jennings — the best player in “Jeopardy!” history, who won 74 games in a row in 2004 — Watson will lose if its performance doesn’t improve. It’s pretty far up in the winner’s cloud, but it’s not yet at Jennings’s level; in the sparring matches, Watson was beaten several times by opponents who did nowhere near as well as Jennings. (Indeed, it sometimes lost to people who hadn’t placed first in their own appearances on the show.) The show’s executive producer, Harry Friedman, will not say whom it is picking to play against Watson, but he refused to let Jennings be interviewed for this story, which is suggestive.
Ferrucci says his team will continue to fine-tune Watson, but improving its performance is getting harder. “When we first started, we’d add a new algorithm and it would improve the performance by 10 percent, 15 percent,” he says. “Now it’ll be like half a percent is a good improvement.”
Ferrucci’s attitude toward winning is conflicted. I could see that he hungers to win. And losing badly on national TV might mean negative publicity for I.B.M. But Ferrucci also argued that Watson might lose merely because of bad luck. Should one of Watson’s opponents land on both Daily Doubles, for example, that player might double his or her money and vault beyond Watson’s ability to catch up, even if the computer never flubs another question.
Ultimately, Ferrucci claimed not to worry about winning or losing. He told me he’s happy that I.B.M. has simply pushed this far and produced a system that performs so well at answering questions. Even a televised flameout, he said, won’t diminish the street cred Watson will give I.B.M. in the computer-science field. “I don’t really care about ‘Jeopardy!’ ” he told me, shrugging.
I.B.M. PLANS TObegin selling versions of Watson to companies in the next year or two. John Kelly, the head of I.B.M.’s research labs, says that Watson could help decision-makers sift through enormous piles of written material in seconds. Kelly says that its speed and quality could make it part of rapid-fire decision-making, with users talking to Watson to guide their thinking process.
“I want to create a medical version of this,” he adds. “A Watson M.D., if you will.” He imagines a hospital feeding Watson every new medical paper in existence, then having it answer questions during split-second emergency-room crises. “The problem right now is the procedures, the new procedures, the new medicines, the new capability is being generated faster than physicians can absorb on the front lines and it can be deployed.” He also envisions using Watson to produce virtual call centers, where the computer would talk directly to the customer and generally be the first line of defense, because, “as you’ve seen, this thing can answer a question faster and more accurately than most human beings.”
“I want to create something that I can take into every other retail industry, in the transportation industry, you name it, the banking industry,” Kelly goes on to say. “Any place where time is critical and you need to get advanced state-of-the-art information to the front of decision-makers. Computers need to go from just being back-office calculating machines to improving the intelligence of people making decisions.” At first, a Watson system could cost several million dollars, because it needs to run on at least one $1 million I.B.M. server. But Kelly predicts that within 10 years an artificial brain like Watson could run on a much cheaper server, affordable by any small firm, and a few years after that, on a laptop.
Ted Senator, a vice president of SAIC — a high-tech firm that frequently helps design government systems — is a former “Jeopardy!” champion and has followed Watson’s development closely; in October he visited I.B.M. and played against Watson himself. (He lost.) He says that Watson-level artificial intelligence could make it significantly easier for citizens to get answers quickly from massive, ponderous bureaucracies. He points to the recent “cash for clunkers” program. He tried to participate, but when he went to the government site to see if his car qualified, he couldn’t figure it out: his model, a 1995 Saab 9000, was listed twice, each time with different mileage-per-gallon statistics. What he needed was probably buried deep inside some government database, but the bureaucrats hadn’t presented the information clearly enough. “So I gave up,” he says. This is precisely the sort of task a Watson-like artificial intelligence can assist in, he says. “You can imagine if I’m applying for health insurance, having to explain the details of my personal situation, or if I’m trying to figure out if I’m eligible for a particular tax deduction. Any place there’s massive data that surpasses the human’s ability to sort through it, and there’s a time constraint on getting an answer.”
Many experts imagine even quirkier ways that everyday life might be transformed as question-answering technology becomes more powerful and widespread. Andrew Hickl, the C.E.O. of Language Computer Corporation, which makes question-answering systems, among other things, for businesses, was recently asked by a client to make a “contradiction engine”: if you tell it a statement, it tries to find evidence on the Web that contradicts it. “It’s like, ‘I believe that Dallas is the most beautiful city in the United States,’ and I want to find all the evidence on the Web that contradicts that.” (It produced results that were only 70 percent relevant, which satisfied his client.) Hickl imagines people using this sort of tool to read through the daily news. “We could take something that Harry Reid says and immediately figure out what contradicts it. Or somebody tweets something that’s wrong, and we could automatically post a tweet saying, ‘No, actually, that’s wrong, and here’s proof.’ ”
CULTURALLY, OF COURSE, advances like Watson are bound to provoke nervous concerns too. High-tech critics have begun to wonder about the wisdom of relying on artificial-intelligence systems in the face of complex reality. Many Wall Street firms, for example, now rely on “millisecond trading” computers, which detect deviations in prices and order trades far faster than humans ever could; but these are now regarded as a possible culprit in the seemingly irrational hourlong stock-market plunge of the spring. Would doctors in an E.R. feel comfortable taking action based on a split-second factual answer from a Watson M.D.? And while service companies can clearly save money by relying more on question-answering systems, they are precisely the sort of labor-saving advance deplored by unions — and customers who crave the ability to talk to a real, intelligent human on the phone.
Some scientists, moreover, argue that Watson has serious limitations that could hamper its ability to grapple with the real world. It can analyze texts and draw basic conclusions from the facts it finds, like figuring out if one event happened later than another. But many questions we want answered require more complex forms of analysis. Last year, the computer scientist Stephen Wolfram released “Wolfram Alpha,” a question-answering engine that can do mathematical calculations about the real world. Ask it to “compare the populations of New York City and Cincinnati,” for example, and it will not only give you their populations — 8.4 million versus 333,336 — it will also create a bar graph comparing them visually and calculate their ratio (25.09 to 1) and the percentage relationship between them (New York is 2,409 percent larger). But this sort of automated calculation is only possible because Wolfram and his team spent years painstakingly hand-crafting databases in a fashion that enables a computer to perform this sort of analysis — by typing in the populations of New York and Cincinnati, for example, and tagging them both as “cities” so that the engine can compare them. This, Wolfram says, is the deep challenge of artificial intelligence: a lot of human knowledge isn’t represented in words alone, and a computer won’t learn that stuff just by encoding English language texts, as Watson does. The only way to program a computer to do this type of mathematical reasoning might be to do precisely what Ferrucci doesn’t want to do — sit down and slowly teach it about the world, one fact at a time.
“Not to take anything away from this ‘Jeopardy!’ thing, but I don’t think Watson really is answering questions — it’s not like the ‘Star Trek’ computer,” Wolfram says. (Of course, Wolfram Alpha cannot answer the sort of broad-ranging trivia questions that Watson can, either, because Wolfram didn’t design it for that purpose.) What’s more, Watson can answer only questions asking for an objectively knowable fact. It cannot produce an answer that requires judgment. It cannot offer a new, unique answer to questions like “What’s the best high-tech company to invest in?” or “When will there be peace in the Middle East?” All it will do is look for source material in its database that appears to have addressed those issues and then collate and compose a string of text that seems to be a statistically likely answer. Neither Watson nor Wolfram Alpha, in other words, comes close to replicating human wisdom.
At best, Ferrucci suspects that Watson might be simulating, in a stripped-down fashion, some of the ways that our human brains process language. Modern neuroscience has found that our brain is highly “parallel”: it uses many different parts simultaneously, harnessing billions of neurons whenever we talk or listen to words. “I’m no cognitive scientist, so this is just speculation,” Ferrucci says, but Watson’s approach — tackling a question in thousands of different ways — may succeed precisely because it mimics the same approach. Watson doesn’t come up with an answer to a question so much as make an educated guess, based on similarities to things it has been exposed to. “I have young children, you can see them guessing at the meaning of words, you can see them guessing at grammatical structure,” he notes.
This is why Watson often seemed most human not when it was performing flawlessly but when it wasn’t. Many of the human opponents found the computer most endearing when it was clearly misfiring — misinterpreting the clue, making weird mistakes, rather as we do when we’re put on the spot.
During one game, the category was, coincidentally, “I.B.M.” The questions seemed like no-brainers for the computer (for example, “Though it’s gone beyond the corporate world, I.B.M. stands for this” — “International Business Machines”). But for some reason, Watson performed poorly. It came up with answers that were wrong or in which it had little confidence. The audience, composed mostly of I.B.M. employees who had come to watch the action, seemed mesmerized by the spectacle.
Then came the final, $2,000 clue in the category: “It’s the last name of father and son Thomas Sr. and Jr., who led I.B.M. for more than 50 years.” This time the computer pounced. “Who is Watson?” it declared in its synthesized voice, and the crowd erupted in cheers. At least it knew its own name.

Clive Thompson, a contributing writer for the magazine, writes frequently about technology and science.

supercomputers, High Performance Computing


Home | TOP500 Supercomputing Sites

www.top500.org/ - 頁庫存檔 - 翻譯這個網頁
In June 2011, the TOP500 ranked K the world's fastest supercomputer, with a rating of over 8 petaflops, and in November 2011, K became the first computer to ...

Lists


TOP500 List Releases ... The 39th TOP500 list will be released ...

November 2010


The 36th edition of the closely watched TOP500 list of the ...

TOP500 List - June 2011 (1-100)


Rank, Site, Computer/Year Vendor, Cores, Rmax, Rpeak, Power. 1 ...

November 2003


Search. Message-passing will become the minority way of ...

TOP500 List - November 2010 ...


Rank, Site, Computer/Year Vendor, Cores, Rmax, Rpeak, Power. 1 ...

November 2011


Japan's “K Computer” maintained its position atop the newest ...


20年前寫的超級電腦介紹 內容會大同小異

Computing From Weather to Warcraft




Published: November 17, 2008
For years, Western governments have used supercomputers to model weapons of nuclear war.

Now a company in China uses the powerful machines to tend the fantasy realms of World of Warcraft.
Supercomputers, which are up to a million times faster than the typical desktop PC, are still staples in the data warehouses of national laboratories and universities in the United States, Japan and Western Europe. But over the last few years, the falling cost of supercomputer systems has allowed a broader range of corporations and institutions, including many in China and India, to buy them for everything from processing movie graphics to searching for oil.
Just 18 months ago, China and India lacked a single system among the 25 fastest in the world. But on the latest list of the 500 fastest computers, released Monday, China nailed the No. 10 spot, standing as the only nation besides the United States in the top 10. India, meanwhile, had the 13th-fastest machine, beating Japan, a longtime leader.
China now claims 15 of the world’s 500 fastest computers. That makes it the top-ranking supercomputing country outside the United States, Western Europe and Japan.
The presence of supercomputers in emerging nations like China and India says as much about those countries’ growing national ambitions as the changing state of science and business.
“These other countries are following behind the U.S. and perhaps some other nations in Western Europe, but they are there,” said Jack Dongarra, a computer scientist at the University of Tennessee who helps maintain Top500, the official list of the fastest supercomputers. “These countries are making a clear statement about their intentions.”
The vast majority of supercomputers are built by I.B.M. and Hewlett-Packard. But China’s top system, located at the Shanghai Supercomputer Center, was assembled by the Chinese manufacturer Dawning. Like many of the fastest machines, the Shanghai system will handle research tasks, which remain the most important role for supercomputers. The ability of these machines to simulate experiments, explosions and the weather makes them crucial in an age when scientific discovery often takes place by manipulating large databases of information instead of running physical experiments.
“They are not buying these machines because they like to burn electricity and heat the air,” said Mark Seager, head of advanced computing at the Lawrence Livermore National Laboratory. “It’s for the simulation capabilities, which will be an important economic driver not just for the U.S., but for anyone else with two neurons to rub together.”
Still, the sharply falling cost of fast computers, which are often created by yoking together thousands of standard servers, makes them attractive to businesses for uses that would have been impractical even a few years ago.
For example, in 2007, the Tata Group, an Indian conglomerate, invested $35 million in a computing subsidiary that built what was then the fourth-fastest system in the world. Tata hopes to turn the machine into the basis of a profitable business with government contracts and work for researchers and companies in fields like nanotechnology, biology and electromagnetics. Tata’s computer is already being used to simulate aircraft designs for Boeing and render animated movies.
“We haven’t recovered our investment yet,” said Sunil Sherlekar, head of the Tata lab. “We don’t expect this to be hugely profitable in the short term, but we understand this is a long-term activity.”
For years, some of the fastest machines in China have belonged to The9, a video game developer that owns the local distribution rights to Blizzard Entertainment’s World of Warcraft franchise. Earlier this year, The9 boasted of hosting more than one million World of Warcraft players online at the same time. To support the complex calculations required to create the game’s graphics, The9 owns more than 10 supercomputer systems.
The list of China’s fastest computers is also filled with systems owned by oil and gas companies, financial firms, research groups and other media companies.
Of all the new entrants to the supercomputing race, China appears the most focused. The government has spent a vast amount of money building out its computing infrastructure, hoping to improve science and industry.
“If you look at China and what they are spending to get ahead, it’s clear this is a national priority,” said Douglas Comer, vice president of research at Cisco Systems. “They are definitely coming from behind, and they know that. They’re hungry.”
New Zealand is the leader in terms of computing capacity per capita, thanks to Weta Digital, a visual-effects company whose founders include the movie director Peter Jackson. Weta, based in the New Zealand capital, Wellington, operates four of the fastest machines on the planet for its work on film franchises like “The Lord of the Rings” and “The Fantastic Four.” Weta also rents out space on its systems to local research labs.
The idea of renting out space on big machines harks back to the early era of computing, when computers were so expensive that customers bought blocks of time on them for specific tasks. Today, a number of companies, like the high-speed computing specialist Cray and the graphics-chip maker Nvidia, are building beefy systems that can sit next to a desk and replicate some of the functions handled by room-sized machines. Nvidia, for example, has started selling deskside machines starting at $10,000 that can process data 250 times faster than a regular PC.
The goal behind such computers is to provide scientists, engineers and artists with direct access to strong machines before they send larger jobs off to supercomputers.
And, of course, there remains a prominent place for machines that can cost more than $100 million and take care of the United States government’s most secret jobs. The current fastest computer in the world is operated by Los Alamos National Laboratory in New Mexico, which uses it to perform classified military work.







IBM Breaks Performance Records Through Systems Innovation
PR-USA.net (press release) - Varna,Bulgaria
Engineers and researchers at the IBM ( NYSE : IBM) Hursley development lab in England and the Almaden Research Center in California have demonstrated ...

High Performance Computing 高效能運算設備


high-performance computing指作科學研究等的高速運算(High-speedcomputing, which typicallyrefers to supercomputers used in scientificresearch.)
某校計算機及資訊網路中心服務說明:
1.適合對象:研究實驗內容需要利用電腦程式計算,且該程式之執行時間、記憶體利用等需求超過一般個人電腦。潛在使用者包括:a)自行使用C/C++/Fortran 等程式語言開發程式之研究團隊。 b) 目前已經透過 MPI OpenMP 等函式庫進行平行化程式撰寫之研究團隊。
2. 預期成效:將提供兩種不 同性質之高效能運算設備: a)SMP大型主機:具備256GB 記憶體,適合單一 程式需要大量記憶體的情況使用。此外對於已經利用OpenMP進行平行化運算的 程式,也可以提供有效幫助。 b)cluster 叢集伺服器,提供超過一百組節點之 運算主機,系統運算能力高達 3TFlops,特別適合已經利用MPI函式庫進行平行化之程式。 c)教育訓練:未來將與廠商及校內教授合作,提供多元化教育訓練予校內研究團隊,期能幫助校內研究團隊熟悉各式主機之使用、程式撰寫與除錯技巧、平行化程式之設計與開發,以便有效協助研究團隊,加速各式實驗之進行。
----
大型的網路公司如古鉤(Google)、電子灣(eBay)及亞馬遜擁有的電腦運算力量,比起任何學術電腦網都要大,而且目前已成為配銷大規模電腦運算服 務的領頭者。這些公司的許多創新技術都可以幫忙科學家,例如今年八月推出的「點播運算」(computing-on-demand)服務正是其一,客戶使 用亞馬遜龐大的電算基礎設施,創設虛擬多重的電腦,每一運算小時只要美金一角;另外,每十億位元組(gigabyte)資訊的儲存費用,每月只要美金一角 五分。......

亞馬遜服務最主要的賣點,在使用「虛擬化科技」(virtualizationtechnologies);很多人預測,這種科技不止會改變學術研究,電腦運算科技本身也會改變。
虛擬化科技使用一套軟體,讓多重的操作系統能夠一起運作。這一點意味著同一部機器上頭,能夠創造出許多不同的電腦。比如單一一部機器,能夠當作十部操作系統各不相同「虛擬」電腦的宿主。
這 一點很了不起。在單一伺服器上運作多重的虛擬電腦,使用資源時有效率得多。此外,這一點還意味著不必安裝有實體、擁有特殊操作系統的機器,只消幾秒鐘, 就能造出虛擬版的電腦來。如此的虛擬電腦可以像檔案般複製,還可以在任何機器上來跑,不管機器用的硬體是什麼。佩斯表示,以往大家每台機器都得裝軟、硬 體,現在則不一定了。
要取得虛擬化軟體,目前愈來愈形便利,比如微軟、Vmware等公司都有提供。亞馬遜使用的軟體叫「仙」(Xen), 這個由英國劍橋大學研發出來、程式碼 開放的系統,很快就在學者間流行起來。學者可以使用「仙」這套系統,可以讓虛擬電腦來操作一整個網格或一整叢的電腦(儘管每台電腦的操作系統都不相同), 還可以使用自己實驗室裡開發出來的應用程式。
--Knowledge Review 知識新知 12/16/2006

亞馬遜出賣運算能力

Intel has overhauled the basic building block of the information age, paving the way for faster and more energy-efficient processors.



---- To me the attempt to redefine it as "High Productivity Computing" is, if I may be to the point, nothing more than a group of people attempting to justify the (probably overpriced) services they are now attempting to sell by a meaningless (from a practical perspective) redefinition of an acronym. I'm kinda surprised that they're not pushing SSHPC (Six Sigma...).



petaflop
IBM Roadrunner, a supercomputer
Wikipedia article "Geococcyx".Road running is the sport of running on a measured course over an established road (as opposed to track and cross country running). These events normally range from 5 km to long distance, such as half marathons and marathons, and may involve large numbers of runners or wheelchair entrants. Road running is part of group of road events known as "road races".

Military Supercomputer Sets Record


Published: June 9, 2008

SAN FRANCISCO — An American military supercomputer, assembled from components originally designed for video game machines, has reached a long-sought-after computing milestone by processing more than 1.026 quadrillion calculations per second.
Skip to next paragraph

I.B.M.
The Roadrunner supercomputer costs $133 million and will be used to study nuclear weapons.

The new machine is more than twice as fast as the previous fastest supercomputer, the I.B.M. BlueGene/L, which is based at Lawrence Livermore National Laboratory in California.
The new $133 million supercomputer, called Roadrunner in a reference to the state bird of New Mexico, was devised and built by engineers and scientists at I.B.M. and Los Alamos National Laboratory, based in Los Alamos, N.M. It will be used principally to solve classified military problems to ensure that the nation’s stockpile of nuclear weapons will continue to work correctly as they age. The Roadrunner will simulate the behavior of the weapons in the first fraction of a second during an explosion.
Before it is placed in a classified environment, it will also be used to explore scientific problems like climate change. The greater speed of the Roadrunner will make it possible for scientists to test global climate models with higher accuracy.
To put the performance of the machine in perspective, Thomas P. D’Agostino, the administrator of the National Nuclear Security Administration, said that if all six billion people on earth used hand calculators and performed calculations 24 hours a day and seven days a week, it would take them 46 years to do what the Roadrunner can in one day.
The machine is an unusual blend of chips used in consumer products and advanced parallel computing technologies. The lessons that computer scientists learn by making it calculate even faster are seen as essential to the future of both personal and mobile consumer computing.
The high-performance computing goal, known as a petaflop — one thousand trillion calculations per second — has long been viewed as a crucial milestone by military, technical and scientific organizations in the United States, as well as a growing group including Japan, China and the European Union. All view supercomputing technology as a symbol of national economic competitiveness.
By running programs that find a solution in hours or even less time — compared with as long as three months on older generations of computers — petaflop machines like Roadrunner have the potential to fundamentally alter science and engineering, supercomputer experts say. Researchers can ask questions and receive answers virtually interactively and can perform experiments that would previously have been impractical.
“This is equivalent to the four-minute mile of supercomputing,” said Jack Dongarra, a computer scientist at the University of Tennessee who for several decades has tracked the performance of the fastest computers.
Each new supercomputing generation has brought scientists a step closer to faithfully simulating physical reality. It has also produced software and hardware technologies that have rapidly spilled out into the rest of the computer industry for consumer and business products.
Technology is flowing in the opposite direction as well. Consumer-oriented computing began dominating research and development spending on technology shortly after the cold war ended in the late 1980s, and that trend is evident in the design of the world’s fastest computers.
The Roadrunner is based on a radical design that includes 12,960 chips that are an improved version of an I.B.M. Cell microprocessor, a parallel processing chip originally created for Sony’s PlayStation 3 video-game machine. The Sony chips are used as accelerators, or turbochargers, for portions of calculations.
The Roadrunner also includes a smaller number of more conventional Opteron processors, made by Advanced Micro Devices, which are already widely used in corporate servers.
“Roadrunner tells us about what will happen in the next decade,” said Horst Simon, associate laboratory director for computer science at the Lawrence Berkeley National Laboratory. “Technology is coming from the consumer electronics market and the innovation is happening first in terms of cellphones and embedded electronics.”
The innovations flowing from this generation of high-speed computers will most likely result from the way computer scientists manage the complexity of the system’s hardware.
Roadrunner, which consumes roughly three megawatts of power, or about the power required by a large suburban shopping center, requires three separate programming tools because it has three types of processors. Programmers have to figure out how to keep all of the 116,640 processor cores in the machine occupied simultaneously in order for it to run effectively.
“We’ve proved some skeptics wrong,” said Michael R. Anastasio, a physicist who is director of the Los Alamos National Laboratory. “This gives us a window into a whole new way of computing. We can look at phenomena we have never seen before.”
Solving that programming problem is important because in just a few years personal computers will have microprocessor chips with dozens or even hundreds of processor cores. The industry is now hunting for new techniques for making use of the new computing power. Some experts, however, are skeptical that the most powerful supercomputers will provide useful examples.
“If Chevy wins the Daytona 500, they try to convince you the Chevy Malibu you’re driving will benefit from this,” said Steve Wallach, a supercomputer designer who is chief scientist of Convey Computer, a start-up firm based in Richardson, Tex.
Those who work with weapons might not have much to offer the video gamers of the world, he suggested.
Many executives and scientists see Roadrunner as an example of the resurgence of the United States in supercomputing.
Although American companies had dominated the field since its inception in the 1960s, in 2002 the Japanese Earth Simulator briefly claimed the title of the world’s fastest by executing more than 35 trillion mathematical calculations per second. Two years later, a supercomputer created by I.B.M. reclaimed the speed record for the United States. The Japanese challenge, however, led Congress and the Bush administration to reinvest in high-performance computing.
“It’s a sign that we are maintaining our position,“ said Peter J. Ungaro, chief executive of Cray, a maker of supercomputers. He noted, however, that “the real competitiveness is based on the discoveries that are based on the machines.”
Having surpassed the petaflop barrier, I.B.M. is already looking toward the next generation of supercomputing. “You do these record-setting things because you know that in the end we will push on to the next generation and the one who is there first will be the leader,” said Nicholas M. Donofrio, an I.B.M. executive vice president.
By breaking the petaflop barrier sooner than had been generally expected, the United States’ supercomputer industry has been able to sustain a pace of continuous performance increases, improving a thousandfold in processing power in 11 years. The next thousandfold goal is the exaflop, which is a quintillion calculations per second, followed by the zettaflop, the yottaflop and the xeraflop.

 Advanced Research Computing @ Cardiff (ARCCA),

 

Making the impossible possible

2 June 2008
Information Services director Martyn Harrow (left) and ARCCA director Professor Martyn Guest with the new Bull High Performance Computer
Information Services director Martyn Harrow (left) and ARCCA director Professor Martyn Guest with the new Bull High Performance Computer
Academics across the University are set to benefit after the launch of a powerful new computing cluster which will enable research projects previously considered too difficult or time-consuming.
Supplied by leading international IT firm Bull, the new computer is one of the most advanced in the UK academic sector.
The computer will be run by Advanced Research Computing @ Cardiff (ARCCA), which was set up to supply all University academics with the high-powered technology necessary to tackle today’s big research questions. Already ARCCA is putting its computing power to work in a wide variety of fields. These include:
  • Health. Working with the new Positron Emission Tomography scanner (PET), able to detect cancers at a smaller size than previous technology. A separate project involves the School of Computer Science and Velindre Cancer Centre in developing more accurate radiotherapy plans for cancer patients.
  • Neuroscience. Working with the Cardiff University Brain Research Imaging Centre to map the structure and function of our brains.
  • Geosciences. Simulating earth mantle and tectonic plate movements to improve our understanding of earthquakes and volcano eruptions.
  • Astrophysics. Recreating the formation of stars and planets, and taking part in the international hunt for gravitational waves.
  • Archaeology. Working with English Heritage to pinpoint the carbon dating of prehistoric sites.
  • Renewable Energy. Working with engineers to model hydrodynamic processes which can be used for tidal and wave power.
The High Performance Computer will support study in all areas of research, including the arts, humanities and social sciences. Cardiff Business School is already working with ARCCA on economic modelling, and the School of English, Communication and Philosophy on linguistics.
The computer uses Intel® Xeon® Quad-core processors, with four cores to each chip. The system has approximately four terabytes (or four million megabytes) of memory and has just been measured as performing 20 trillion floating point operations a second (20 Teraflops). These results have yet to be officially ratified but would make it the most powerful cluster in a UK University dedicated to in-house research. It was funded with a Science Research Investment Funding (SRIF) grant from the Higher Education Funding Council Wales
The computer will not only be one of the most powerful at a British University, but also one of the greenest. Based in its own state-of-the-art data centre, it is housed in ten energy efficient water-cooled racks, saving around £30,000 a year on conventional air cooling systems.
Cardiff’s partnership with Bull will continue with the creation of the Cardiff High Performance Computing Centre of Excellence, based around the new computer. The Centre will extend the scope and quality of computer-based research support and open up a range of new research frontiers.
Launching the High Performance Computer at the University, Welsh Assembly Government First Minister, Rhodri Morgan said: “The developments in High Performance Computing brought about by ARCCA are already making huge differences in many areas of research. This puts Cardiff University at the forefront of computer-based research techniques in Wales and the UK, as well as internationally.”
Bull High Performance Computer
Didier Lamouche, Bull Chief Executive Officer, said: “Being involved in this partnership with Cardiff University has enabled us to demonstrate the importance of utilising leading-edge IT resources to pursue ground-breaking research. This new supercomputer will support Cardiff’s growing reputation as one of the most innovative, ambitious and successful universities in the country and internationally.”
The Vice-Chancellor of Cardiff University, Dr David Grant, said: “The technical specifications of the Bull High Performance Computer are extremely impressive. We expect the research enabled by this computational power will be more impressive still. Computer modelling is becoming vital to our understanding of human biology and the development of new drugs. Simulation will bring major benefits in the sciences and engineering, and open up completely new research fields in the arts, humanities and social sciences. The new Cardiff High Performance Computing Centre of Excellence will keep the University at the forefront of these exciting possibilities in all of its academic disciplines.”
Professor Martyn Guest, Director of ARCCA, said: “ARCCA exists to deliver high-end computing service, resources and support to all researchers in all disciplines and Schools. The new High Performance Computer will allow a wide variety of studies previously dismissed as impossible or impractical. We look forward to talking to academics from all fields about how ARCCA can help them achieve their research objectives.”

Related links

 幾秒鐘完成全年功課的超級電腦

超級計算機
卡迪夫大學新置的超級計算機計算速度可達每秒20 teraflops
英國加的夫大學(Cardiff University
)正式啟用一台新的超級計算機,計算能力據稱足以在幾秒鐘內完成英國全國所有大、中、小學生一年的作業。
這將是英國所有大學當中計算速度排行第三的計算機,預料可望進入全球50強的行列。
價值290萬英鎊的這台高性能計算機是法國布爾(Bull)集團提供的,計算能力可達每秒鐘20萬億次浮點運算(20 teraflops)。
諾貝爾獎得主
加的夫大學是英國研究能力較強的學府之一,現有兩名諾貝爾獎得主:其一是獲得2007年醫學獎的馬丁•埃文思教授(Prof Martin Evans),另一位是1988年化學獎得主羅伯特•休伯爾教授(Prof Robert Huber)。
超級計算機將由一個新設立的中心操作管理,為大學各學科提供計算服務。
應用領域

    
在醫學領域,它將能協助正電子斷層掃描儀(Positron Emission Tomography-PET)檢測出更小範圍的癌病變。
    
計算機也將用於該校正與威爾士韋林德爾(Velindre)腫瘤中心一起研究的放射治療照射量的計算,以便給病人提供量度更精確的照射治療。
    
在神經科學領域,加的夫大學的人腦研究掃描中心可望使用這台計算機繪製人腦結構與功能圖。
    
這台超級計算機也會用於考古研究。目前該校正與英格蘭文化遺產協會(English Heritage)合作,展開對遠古遺址年代的炭鑑定。
    
可再生能源的研究也是這台電腦效力的領域,譬如把它用於製作水利動力過程的模型。這類模型也可用於開發洪水預報的技術。
加的夫大學校長戴維•格蘭特(David Grant)說,計算機模型在當今的生理學研究和新藥開發方面已在扮演必不可少的角色。
“模擬技術將給科學和工程領域帶來巨大效益,也會給藝術、人文和社會科學開拓嶄新的科研領域。”

几秒钟完成全年功课的超级电脑
超级计算机
卡迪夫大学新置的超级计算机计算速度可达每秒20 teraflops
英国加的夫大学(Cardiff University

)正式启用一台新的超级计算机,计算能力据称足以在几秒钟内完成英国全国所有大、中、小学生一年的作业。
这将是英国所有大学当中计算速度排行第三的计算机,预料可望进入全球50强的行列。
价值290万英镑的这台高性能计算机是法国布尔(Bull)集团提供的,计算能力可达每秒钟20万亿次浮点运算(20 teraflops)。
诺贝尔奖得主
加的夫大学是英国研究能力较强的学府之一,现有两名诺贝尔奖得主:其一是获得2007年医学奖的马丁•埃文思教授(Prof Martin Evans),另一位是1988年化学奖得主罗伯特•休伯尔教授(Prof Robert Huber)。
超级计算机将由一个新设立的中心操作管理,为大学各学科提供计算服务。
应用领域
  • 在医学领域,它将能协助正电子断层扫描仪(Positron Emission Tomography-PET)检测出更小范围的癌病变。
  • 计算机也将用于该校正与威尔士韦林德尔(Velindre)肿瘤中心一起研究的放射治疗照射量的计算,以便给病人提供量度更精确的照射治疗。
  • 在神经科学领域,加的夫大学的人脑研究扫描中心可望使用这台计算机绘制人脑结构与功能图。
  • 这台超级计算机也会用于考古研究。目前该校正与英格兰文化遗产协会(English Heritage)合作,展开对远古遗址年代的炭鉴定。
  • 可再生能源的研究也是这台电脑效力的领域,譬如把它用于制作水利动力过程的模型。这类模型也可用于开发洪水预报的技术。
加的夫大学校长戴维•格兰特(David Grant)说,计算机模型在当今的生理学研究和新药开发方面已在扮演必不可少的角色。
“模拟技术将给科学和工程领域带来巨大效益,也会给艺术、人文和社会科学开拓崭新的科研领域。”

2012年6月12日 星期二

oldest galaxy


 

Japanese astronomers use telescope on Hawaii volcano to detect what could be oldest galaxy




By Associated Press, Tuesday, June 12, 3:03 AM
HONOLULU — A team of Japanese astronomers using telescopes on Hawaii say they’ve seen the oldest galaxy, a discovery that’s competing with other “earliest galaxy” claims.

The Japanese team calculates its galaxy was formed 12.91 billion light-years ago, and their research will be published in the Astrophysical Journal. The scientists with the National Astronomical Observatory of Japan used the Subaru and Keck telescopes on the summit of Mauna Kea.

A light-year is the distance that light travels in a year, about 6 trillion miles. Seeing distant galaxies is akin to looking back into time.

Richard Ellis of the California Institute of Technology, an influential expert in cosmology and galaxy formation, said the latest work as more convincing than some other galaxy discoveries.

He said the Japanese claim is more “watertight,” using methods that everyone can agree on. But he said it’s not much of a change from a similar finding by the same team last year.
Still, “it’s the most distant bullet-proof one that everybody believes,” Ellis said.

In 2010, a French team using NASA’s Hubble Space Telescope claimed to have discovered a galaxy from 13.1 billion light-years ago and last year a California team using Hubble said they saw a galaxy from 13.2 billion light-years ago. Both Hubble teams published findings in the journal Nature.

However, the two Hubble teams have yet to confirm their findings with other methods, said Ellis. Also, a team of Arizona State University astronomers this month claimed to have found a galaxy from 13 billion light-years away. They used a telescope in Chile.

Current theory holds that the universe was born of an explosion, called the Big Bang, about 13.7 billion years ago. So astronomers using the most powerful telescopes available are peering deeper and deeper into that dawn of the universe.

Copyright 2012 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.

2012年6月5日 星期二

National Lending Library for Science and Technology

National Lending Library for Science and Technology

晚上 見高等教育老闆很認真在更新電腦 與他聊天 因有本錢三強論文集 要賣900多   跟老闆談倫敦有專收技圖書館 我1978年寫論文時曾造訪過 (台灣採取補助立大學專案買書方式 (不限技))


由於1977-78年留英時的資料都找不到了
所以我試找當年倫敦的專門科技圖書館 我是去找 National Physical Lab. 關於Calibration方面的資料
現在網路的第一頁竟然只有 國家圖書館的科技書外借部
 所以姑且先貼上 以後再查考


http://www.bl.uk/aboutus/quickinfo/facts/history/index.html

National Lending Library for Science and Technology

The third major component of the British Library consisted of the National Central Library or NCL which began operation in 1916 in London and the National Lending Library for Science and Technology (NLLST), in service since 1961 at Boston Spa in Yorkshire. These were amalgamated in 1973 as the British Library Lending Division (BLLD).
The function of the Lending Division was to support the library systems of the UK by providing a loan and photocopy service to other libraries throughout the country.
The NLLST had a stock specialising in science and technology, containing 25,000 monographs and subscriptions to 1,200 serials; its staff numbered 120. Around 600 tons of the NCL stock, which specialised in humanities and social sciences, was transferred to Yorkshire during the Library's first year of formation. The semi-rural site at Boston Spa occupies around 60 acres of an ex-munitions factory and is well served by road links for easy distribution.
During the 1970s the range of services was expanded and made available to international customers and use of technology became a more integral part of the service. The use of Automated Requesting grew by about 40% in this time and the Lending Division often acted in collaboration with academic and scientific partners in early days of exploring the future of fax transmission and satellite communications.
In 1985, the title was changed to the British Library Document Supply Centre to reflect the changing emphasis of document supply in which a greater proportion of requests were for copies of articles rather than loans. The stock has grown over the years and now contains over 260,000 journal titles, over 3 million books, almost 500,000 conference proceedings and almost 5 million reports, mostly of a scientific nature.
Current business from document supply totals about 4,000,000 requests per year from 20,000 customers worldwide. In 2001 the 100 millionth request was received. Services are now provided not just to the traditional customer base of UK and international librarians and information professionals, but also to commercial and business users and individual researchers. Use of the Web has provided direct access to our collection information and supply services, and location is no longer an issue for distribution, as document supply moves increasingly to electronic delivery.