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.

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.

Venus Sun transit, The transit of science




Live Streaming Video of Venus Transit
Skywatchers around the world can begin looking for Venus to start crossing the face of the sun, a rare event that won’t come around again for 105 years.

 

East Asia to see Venus Sun transit

Planet Venus puts on a show for skywatchers worldwide, moving across the face of the Sun in a transit that will not be seen again for another 105 years.



 http://www.bbc.co.uk/news/science-environment-18293989

The transit of science

Transit of Venus

Related Stories

"This is Philosophical Transactions from 1716, and Halley's paper - which is in Latin - is number five in the volume". 

Taking care not to damage the brittle, yellowing pages the Royal Society's chief archivist and librarian Keith Moore turns to one of the seminal scientific papers in both the Society's - and science's - history. Edmund Halley's 1716 essay - A New Method of Determining the Parallax of the Sun.

Figure for the Transit of Venus by Edmond Halley, from Philosophical Transactions of the Royal Society, 1716. Figure for the Transit of Venus by Edmond Halley, from Philosophical Transactions of the Royal Society, 1716.
 
Although Halley wasn't the first to observe a Transit of Venus - when the planet passes in front of the sun - his paper threw down a gauntlet to the then nascent scientific community. A challenge to use the pair of transits predicted to occur in 1761 and 1769 to transform astronomy into a fully fledged empirical science by calculating the distance of the earth to the sun.

It was a call to arms that would launch a thousand ships according to Andrea Wulf, the author of Chasing Venus: The Race to Measure the Heavens. Astronomers from all over the world took up the challenge, organising expeditions and overcoming enormous obstacles to make their observations from the farthest corners of the globe.

"This is a time when clocks are still not precise enough to determine longitude," she says. "A time when a letter from Philadelphia to London takes two or three months to arrive."
"It was clear right from the beginning that it would have to be an international scientific collaboration. Countries which were at war would have to work together. Just the logistics of it must have been absolutely extraordinary."

Halley's idea was to observe and measure the transits from separate points, as far apart as possible, giving a wide base-line from which to use basic trigonometry to calculate the distance between Venus and the earth.

Because astronomers had a good idea about the relative distances between the celestial bodies, it should be possible to use this one empirical measurement to calculate the dimensions of the entire solar system.

"What the observers of the Transit of Venus were after was the fundamental unit of astronomical measurement" according to the Oxford astronomer Dr Chris Lintott.
"Once you get that then the scale of everything else falls into place, and that's why there was this huge effort and enthusiasm to travel to the far ends of the earth to get this one fundamental measurement."

Cannot play media. You do not have the correct version of the flash player. Download the correct version
From Jean Chappe's gruelling trek across Siberia, to Captain Cook's voyage to Tahiti (and eventual discovery of Australia), the swashbuckling exploits of the transit scientists read like the chapters in a boys-own adventure story.

Charles Mason and Jeremiah Dixon (later to divide America along the Mason-Dixon line) team up for the first time at the Cape of Good Hope, while Guillaume Le Gentil spends 11 years travelling the globe, sees only cloudy skies, and returns home to find his heirs have declared him dead and divided his estate.

But perhaps the most enduring legacy of the transit decade, Andrea Wulf argues, was the model of collaborative international scientific research and exploration it helped to forge.
"The whole idea of a modern scientific expedition is formed in this time. From now on if you go to another place on the globe, its accepted that you have to take a scientific team with you. So we get Charles Darwin on the Beagle, and even Napoleon, when he invades Egypt, takes two hundred scholars with him. It all stems from this single endeavour to measure the size of the solar system."

And transits still form a vital component in the astronomer's arsenal. The Kepler Space Telescope, launched in 2009, uses the tell-tale dip in the luminosity of distant stars to spot extra-solar planets orbiting in front of them. To date it's spotted 61 new worlds, with a further 2321 planet candidates awaiting confirmation.

You can find out more about the events and viewing opportunities for next week's Transit of Venus here, and while you're watching that small black dot march across the surface of the sun, spare a thought for the adventurers and explorers who travelled to the farthest corners of the globe to measure the transits of 1761 and 1769.

They put a figure of 93,726,900 miles on the distance from the earth to the sun - less than 800,000 miles off today's calculation - and built a model for international scientific collaboration that endures to this day. An astonishing achievement.