June 30th, 2008 2008年6月30日 A lot has been written lately on how intelligent search will solve all kinds of problems, most recently in很多已經寫入最近就如何智能搜索將解決各種問題，最近一次是在 The End of Theory, Chris Anderson結束理論，克里斯安德森 of “long tail” fame confuses the abundance of low hanging fruit that “big search” and biotechnologies provide with the ability to really understand and extract meaning, pose and falsify or support hypothesies.對“長尾巴”名利混淆了豐富的低掛水果“大搜索”和生物技術提供的能力，真正理解和提取的含義，構成和偽造或支持hypothesies 。 Mathew Ingram takes issue with the Wired article in馬修英格拉姆問題需要與有線文章 Google and the end of everything谷歌和一切的結束， and Alistair Croll piles on in和阿利斯泰爾克羅爾在樁 Does Big Search change science?難道大搜索變化科學？ emphasizing the familiar scientific refrain: correlation does not necessitate causation.強調科學不熟悉：相關並不一定因果關係。
To be fair to Chris, it seems that he does understand Mathew’s point that correlation is not causation, rather his thesis seems to be that with sufficiently large datasets and powerful computational algorithms, correlation approaches causation.要公平地克里斯，似乎他並不了解馬修的觀點，即沒有相關的因果關係，而不是他的論文似乎是與足夠大的數據和強大的計算算法，相關辦法因果關係。 However I side with Mathew and Alistair, I don’t think Chris understands what Google or Rapid gene sequencing bring to scientific analysis, or he has written an excellent satirical article:然而，我一邊與馬修和阿利斯泰爾，我不認為克里斯明白谷歌或快速基因測序將科學的分析，或者他寫一個極好的諷刺文章：
Petabytes allow us to say: “Correlation is enough.” We can stop looking for models.容量讓我們說： “相關性是不夠的。 ”我們可以停止尋找模式。 We can analyze the data without hypotheses about what it might show.我們可以分析數據，推測它可能會顯示。 We can throw the numbers into the biggest computing clusters the world has ever seen and let statistical algorithms find patterns where science cannot.我們可以扔掉的數字變成最大的集群計算的世界從未見過，並讓統計算法，找到科學的模式下不能。
It sounds like we should be able to just sit back and feed the raw data into a massive cloud computer, grab a few coffees, live a few lifetimes and get some answers (這聽起來像我們應該能夠公正坐下來和飼料的原始數據進入了大規模雲計算機，抓斗一些咖啡，過幾年的壽命並獲得了一些答案（ Deep Thought深層思考 anyone?).任何人？ ） 。 As the search technology gets smarter we can all afford to get a lot stupider, as we are no longer required to solve scientific problems.隨著搜索技術得到更聰明，我們都可以負擔得到了很多stupider ，因為我們不再需要解決的科學問題。
In actuality Google’s pagerank algorithm(s) and Craig Venter’s DNA shotgun sequencing techniques are successful because they are overly simplistic, designed to capture low hanging fruit as quickly as possible, they don’t solve the hard problems - rather they get us faster down a road that leads to more questions.實際上谷歌的PageRank算法（ s ）和克雷格文特爾DNA的鳥槍法測序技術是成功的，因為它們過於簡單化，旨在捕捉低掛果樹盡快，他們並不很難解決的問題-而他們得到我們更快了道路，導致更多的問題。 Questions that are likely too complicated for either search engines or cute biotech tricks to answer.問題很可能過於複雜，對任何搜索引擎或可愛的生物技術手段來回答。 Requiring experiments and analyses that are too intricate and error-sensitive…that need to be hand-held, coaxed and cajoled.需要實驗和分析，太複雜，誤差敏感...需要手提式，哄騙和引導。 Science in the real world is so different from the platonic model that is taught in schoolbooks.科學在現實世界中是如此不同，柏拉圖式的模式，講授課本。 Failure is important, errors are crucial and we progress because human thought is remarkably adaptable and resilient in the face of this.失敗是很重要的，錯誤是至關重要的，我們取得進展，因為人類思想的顯著適應性和彈性，面對這一點。 Contrast this to the types of problems we will get when our analysis is guided by bug ridden computer algorithms, infested with worms, and the data is riddled with errors and spam.對比這類型的問題，我們會在我們的分析是錯誤指導下充滿計算機算法，佈滿蠕蟲和數據是充滿了錯誤和垃圾郵件。
Until the computing power and the algorithms which guide it, are truly evolutionarily designed, I don’t think science will learn much from the computer.在此之前的計算能力和算法的指導，是真正的進化設計的，我不認為科學會學到很多東西的電腦。 When we do get the kind of AI that Chris and the Google founders are looking for, I suspect that they will find it impossible to clock that type of artificial intelligence at Gigahertz speeds, and that we may end up re-evolving a computer that looks and acts very similar to the human brain.當我們得到什麼樣的人工智能克里斯和谷歌的創始人正在尋找，我懷疑，他們將發現自己不可能找到時鐘這種類型的人工智能在千兆赫的速度，我們最終可能重新演變的計算機期待和行為非常相似，人類的大腦。 At which point we may regret not using the ones we already have instead.在這一點上，我們可能會後悔沒有用的，我們已經有代替。
For the next stop on this train of thought, read the excellent article對於下一站的這一思路，閱讀優秀的文章 Is Google Making us Stupid?谷歌是我們愚蠢的決策？ I’ve got one foot in the YES camp.我還有一隻腳的是營地。
Addendum: the Wired article bothered me as an epitome of reductionist scientific thought. 增編：有線第困擾我作為一個縮影，還原科學思想。 Reductionism by nature tends to focus on the simple problems, hard problems which are complex and expensive to tackle are avoided which leads to the amplification of reductionist techniques and causes. 還原的性質往往側重於簡單的問題，很難的問題，這些問題複雜和昂貴的處理避免導致的放大還原技術和原因。 Sooner or later you might be convinced that all knowledge is within the reach of such reductionist approaches. 遲早您可能會相信，所有的知識範圍內達到這種簡單化的做法。 There is a disturbing correlated trend for industry funding of scientific research to further skew science by leaving problems without obvious economic payoffs by the wayside. 有一個令人不安的趨勢，相關行業資金的科學研究，以進一步傾斜離開科學的問題，沒有明顯的經濟回報了次要位置。 I would suggest that both industrial and reductionist science are represented in the Wired hypothesis. 我認為，工業和還原科學的代表在有線假說。
Posted in張貼於 news新聞 | 1 Comment » 1條評論»
