工程師何去何從：學(xué)習(xí)越多越迷茫？

時(shí)間：2011-04-23 23:28:54

關(guān)鍵字：工程師 BSP RSQUO TIMES

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[導(dǎo)讀] 　　一位已退休的長(zhǎng)輩（他可是一位化學(xué)博士）經(jīng)常告誡我，“了解得越多，所知越有限”（the more we know， the less we know）?！　∮捎谖覀儾粩嗟乜朔酝囊恍┘夹g(shù)障礙，整個(gè)系統(tǒng)正變得越來越

　　一位已退休的長(zhǎng)輩（他可是一位化學(xué)博士）經(jīng)常告誡我，“了解得越多，所知越有限”（the more we know， the less we know）。

　　由于我們不斷地克服以往的一些技術(shù)障礙，整個(gè)系統(tǒng)正變得越來越復(fù)雜，同時(shí)也更加的不可預(yù)測(cè)。

　　·最近的一個(gè)例子是來自美國(guó)國(guó)道交通安全管理局（NHTSA）和美國(guó)太空總署（NASA）工程與安全中心（NESC）針對(duì)豐田（Toyota）汽車突然加速事件的安全性調(diào)查報(bào)告。對(duì)此，EETimes記者M(jìn)ichael Barr先前已發(fā)表過詳實(shí)的報(bào)導(dǎo)。簡(jiǎn)單來說，NASA表示無法確認(rèn)但也不排除軟件問題是造成豐田汽車不正常加速的罪魁禍?zhǔn)住?/p>

　　·Green Hills公司技術(shù)長(zhǎng)Dave Kleidermacher曾經(jīng)在博客上討論使智能手機(jī)更安全可靠的平臺(tái)。透過全國(guó)性電視廣告來宣傳以手機(jī)遠(yuǎn)距離發(fā)動(dòng)汽車是如何地不可思議──這難道是只有我一個(gè)人還是大家都覺得一整個(gè)方式十分愚蠢呢？不過，十多歲的青少年或是像美國(guó)黑帽樂隊(duì)（Black Hat）才會(huì)欣賞這種方式吧！

　　·紐約時(shí)報(bào)（The New York Times）先前發(fā)布過有關(guān)駭客入侵時(shí)代廣場(chǎng)（TimesSquare）電子看板的訊息。第一則駭客的行為最后被發(fā)現(xiàn)只是一種為電影宣傳的手法──實(shí)際上只是網(wǎng)路上流傳的一則YouTube短片，而第二則可是真正以手機(jī)控制了這些電視屏幕，只不過時(shí)間有點(diǎn)短罷了。

　　·史丹佛大學(xué)大學(xué)在其Facebook頁面上描述了工程師們?nèi)绾谓鉀Q復(fù)雜且無法預(yù)測(cè)的“氣動(dòng)彈性顫振”（aeroelastic flutter）問題。（小心！如果你剛好在飛機(jī)上用WiFi看這則文章的話，千萬別點(diǎn)選觀看這則視訊短片。）

　　何去何從？

　　軟件（和硬件）越復(fù)雜，就越難為其定型或找到極端案例（corner case）。我們對(duì)于已知的“未知”似乎就已經(jīng)難以進(jìn)行評(píng)估了，至于如何預(yù)測(cè)不可知的未知，當(dāng)然就更加完全一無所知。

　　我們只知道競(jìng)相攀爬莫測(cè)高深的“抽象階梯”（abstraction ladder），以期擁抱設(shè)計(jì)復(fù)雜性，但卻也制造了不少問題。我最近參加了在鳳凰城（Phoenix，AZ）所舉行的大學(xué)工程系年會(huì)，會(huì)中有一位來自業(yè)界的提問人在一群學(xué)術(shù)界人士為主的座談上直搖頭──能夠培養(yǎng)出了解理論又會(huì)處理抽象問題的真正聰明學(xué)生固然不錯(cuò)，但如果他們?nèi)狈こ虒W(xué)的基本概念，將來進(jìn)入業(yè)界后，公司還是必須重新訓(xùn)練或再教育他們。

　　在預(yù)測(cè)不可知的未知世界時(shí)，我們?nèi)绾巫龅酶茫坑幸环N正規(guī)的方法可循嗎？難道不可能嗎？

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Original：

　　The uncertainty principle

　　--The Article is quoted from Brian Fuller''s Blog

　　In his dotage， my old man （a chemistry Ph.D） used to say， as he slowly stirred his martini with a crooked finger， “the more we know， the less we know.”

　　As systems become increasingly complex because we overcome old technological hurdles， they also become more unpredictable.

　　One recent example is report that NHTSA and the NASA Engineering and Safety Center （NESC） published regarding unintended acceleration of Toyota automobiles. Michael Barr has an excellent report on it. In short， NASA said it couldn’t rule in but couldn’t rule out software problems as a culprit in the unintended acceleration problem.

　　Dave Kleidermacher blogged about making smart phones trustworthy platforms. Is it just me or is it complete lunacy to run a national TV advertising campaign touting the wonders of starting your car remotely with a cell phone？ Your teenage daughter may appreciate the gesture but so too do legions of black hats. （See Kleidermacher’s earlier post on smart phones and security.

　　The New York Times had a pair of posts this week about hacking a Times Square video screen. The first hacking attempt turned out to be a hoax but a really popular YouTube video; the second a real attempt that came up a bit short.

　　And Stanford， via its Facebook page， has described how engineers are addressing the “aeroelastic flutter” problem， a complicated， unpredictable phenomenon. （P.S. don‘t watch this video if you happen to be on a plane with WiFi）。

　　What’s ahead？

　　The more complex the software （and hardware）， the harder it is to model and find corner cases. We seem to be falling behind in assessing the known unknowns and we’re completely in the dark about how to approach unknown unknowns.

　　We race up the abstraction ladder to try to keep our arms around design complexity， but that creates other issues. I attended the annual meeting of college engineering departments recently in Phoenix and one questioner from industry stood before a panel of academics shaking his head. It’s great to turn out really smart kids who know theory and can deal with abstraction， but if they struggle with basic engineering concepts， companies need to train （or retrain， perhaps） them.

　　How are we going to get better at anticipating the unknown unknowns？ It is formal methods？ It is impossible？

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