I'd like to tell you about two games of chess. The first happened in 1997, in which Garry Kasparov, a human, lost to Deep Blue, a machine. To many, this was the dawn of a new era, one where man would be dominated by machine. But here we are, 20 years on, and the greatest change in how we relate to computers is the iPad, not HAL.
我想要告訴你們兩場關於西洋棋競賽的故事。 第一個故事是在1997年,蓋瑞.卡斯帕洛夫(Garry Kasparov), 一個人類,輸給了深藍(Deep Blue),一台機器。 對大多數人來說,這是劃時代的曙光, 一個人們將被機器支配的時代。 但是現在如今的我們,20年後的今天,和電腦之間的關係 最大的改變是iPad, 不是海兒(HAL,小說「太空漫遊」的恐怖超級電腦)。
The second game was a freestyle chess tournament in 2005, in which man and machine could enter together as partners, rather than adversaries, if they so chose. At first, the results were predictable. Even a supercomputer was beaten by a grandmaster with a relatively weak laptop. The surprise came at the end. Who won? Not a grandmaster with a supercomputer, but actually two American amateurs using three relatively weak laptops. Their ability to coach and manipulate their computers to deeply explore specific positions effectively counteracted the superior chess knowledge of the grandmasters and the superior computational power of other adversaries. This is an astonishing result: average men, average machines beating the best man, the best machine. And anyways, isn't it supposed to be man versus machine? Instead, it's about cooperation, and the right type of cooperation.
第二個競賽是自由式西洋棋聯賽。 在2005年,人們和機器可以一起參賽, 成為夥伴,更勝成為對手,如果他們願意的話。 首先,這個比賽結果是可以預料的。 就算是一台超級電腦,也會敗給使用遜色許多之筆記型電腦的棋聖。 就算是一台超級電腦,也會敗給使用遜色許多之筆記型電腦的棋聖。 令人咋舌的是在最後結果。誰贏了呢? 不是使用超級電腦的棋聖, 但,事實上,是兩位使用三台較遜色的筆記型電腦美國業餘選手。 但,事實上,是兩位使用三台較遜色的筆記型電腦美國業餘選手。 他們的能力是訓練並控制他們的電腦 去深度的探究精確的棋位, 有效地抗擊具備棋聖和超級電腦運算能力的高階西洋棋知識對手。 有效地抗擊具備棋聖和超級電腦運算能力的高階西洋棋知識對手。 有效地抗擊具備棋聖和超級電腦運算能力的高階西洋棋知識對手。 這是一個驚艷的結果:平庸的人, 平庸的機器,打敗了最強的人,最強的電腦。 但,這一切不本該是人類機器的比劃嗎? 取而代之的是,這是一個關於合作,一個對的合作形式。
We've been paying a lot of attention to Marvin Minsky's vision for artificial intelligence over the last 50 years. It's a sexy vision, for sure. Many have embraced it. It's become the dominant school of thought in computer science. But as we enter the era of big data, of network systems, of open platforms, and embedded technology, I'd like to suggest it's time to reevaluate an alternative vision that was actually developed around the same time. I'm talking about J.C.R. Licklider's human-computer symbiosis, perhaps better termed "intelligence augmentation," I.A.
過去五十年,我們花掉了太多關注在馬文.明斯基(Marvin Minsky)的人工智慧觀點。 過去五十年,我們花掉了太多關注在馬文.明斯基(Marvin Minsky)的人工智慧觀點。 這是引人入勝的願景,也當然,許多人更懷此大志。 這個願景也成了大多數學校對於電腦科學的中心思想。 但當我們進入大量資料、大量網路系統、許多開放平台和嵌入性科技的時代, 但當我們進入大量資料、大量網路系統、許多開放平台和嵌入性科技的時代, 我會想諫言,是時候該重新評估替代性願景了, 這個願景事實上也是來自於同一個時代背景的。 我說的是關於力克利德(J.C.R. Licklider)的人類與電腦之合作關係, 或許最好稱作,「智能強化」 I.A.
Licklider was a computer science titan who had a profound effect on the development of technology and the Internet. His vision was to enable man and machine to cooperate in making decisions, controlling complex situations without the inflexible dependence on predetermined programs. Note that word "cooperate." Licklider encourages us not to take a toaster and make it Data from "Star Trek," but to take a human and make her more capable. Humans are so amazing -- how we think, our non-linear approaches, our creativity, iterative hypotheses, all very difficult if possible at all for computers to do. Licklider intuitively realized this, contemplating humans setting the goals, formulating the hypotheses, determining the criteria, and performing the evaluation. Of course, in other ways, humans are so limited. We're terrible at scale, computation and volume. We require high-end talent management to keep the rock band together and playing. Licklider foresaw computers doing all the routinizable work that was required to prepare the way for insights and decision making.
力克利德(Licklider)是一個對科技和網際網路的發展上有都很深遠的影響電腦科學巨人。 力克利德(Licklider)是一個對科技和網際網路的發展上有都很深遠的影響電腦科學巨人。 他的願景是推動人類和機器合作,在預先設定的程式中允許彈性互賴,完成決策、控制複雜的狀況。 他的願景是推動人類和機器合作,在預先設定的程式中允許彈性互賴,完成決策、控制複雜的狀況。 他的願景是推動人類和機器合作,在預先設定的程式中允許彈性互賴,完成決策、控制複雜的狀況。 他的願景是推動人類和機器合作,在預先設定的程式中允許彈性互賴,完成決策、控制複雜的狀況。 注意這個字「合作」 力克利德(Licklider)鼓勵我們不僅只是拿一台烤麵包機 當作「星艦奇航記」裡的構想, 更要運用人類去使它更具性能。 人類是如此奇妙,我們的思考方式, 我們的非線性求解,我們的創造力, 演算性假設,對於電腦來說,就算真有可能達到,也是極具困難的。 演算性假設,對於電腦來說,就算真有可能達到,也是極具困難的。 力克利德(Licklider)直覺地瞭解到這一點,考慮了人們會 設定目標、形成假設、 決定標準、並且執行評估。 當然,在別的部份,人類是極度受限的。 我們在比率,計算,和量額的表現極差。 我們需要高階的才華管理 來保持一個搖滾樂團在一起並能夠演奏。 力克利德(Licklider)預見了電腦能完成所有需要事前準備的例行作業的洞見並做出最後決策。 力克利德(Licklider)預見了電腦能完成所有需要事前準備的例行作業的洞見並做出最後決策。
Silently, without much fanfare, this approach has been compiling victories beyond chess. Protein folding, a topic that shares the incredible expansiveness of chess — there are more ways of folding a protein than there are atoms in the universe. This is a world-changing problem with huge implications for our ability to understand and treat disease. And for this task, supercomputer field brute force simply isn't enough. Foldit, a game created by computer scientists, illustrates the value of the approach. Non-technical, non-biologist amateurs play a video game in which they visually rearrange the structure of the protein, allowing the computer to manage the atomic forces and interactions and identify structural issues. This approach beat supercomputers 50 percent of the time and tied 30 percent of the time. Foldit recently made a notable and major scientific discovery by deciphering the structure of the Mason-Pfizer monkey virus. A protease that had eluded determination for over 10 years was solved was by three players in a matter of days, perhaps the first major scientific advance to come from playing a video game.
悄悄的,並沒有太多的誇耀,這個模式已經形成無數次遠超越西洋棋的成功。 悄悄的,並沒有太多的誇耀,這個模式已經形成無數次遠超越西洋棋的成功。 蛋白質構型,一個與廣闊的西洋棋法一樣驚人的主題 蛋白質構型的方式比目前在宇宙的原子數目還要多。 對於我們了解並治癒疾病的能力來說,這是一個能夠改變世界,帶來重大影響的課題。 對於我們了解並治癒疾病的能力來說,這是一個能夠改變世界,帶來重大影響的課題。 在這個任務上,超級電腦存在的計算蠻力是絕對不夠的。 佛迪特(Foldit),一個由電腦科學家發明的遊戲, 圖解了這個方法的價值。 不具科技背景,不具生物學背景的業餘者運用了電玩,使他們可以用視覺去重新排列蛋白質的結構, 不具科技背景,不具生物學背景的業餘者運用了電玩,使他們可以用視覺去重新排列蛋白質的結構, 並允許電腦去管理原子的各種力量、交互作用與結構的鑑定議題。 並允許電腦去管理原子的各種力量、交互作用與結構的鑑定議題。 這個方式有五成的狀況下以時間打敗超級電腦 而且有三成的狀況所用的時間與超級電腦並駕齊驅。 佛迪特(Foldit)最近藉著破解梅森輝瑞猴病毒(MPMV, Mason-Pfizer Monkey Virus)的結構產生一個顯著且重大的科學發現。 佛迪特(Foldit)最近藉著破解梅森輝瑞猴病毒(MPMV, Mason-Pfizer Monkey Virus)的結構產生一個顯著且重大的科學發現。 有一個十多年來已經無法被測定的蛋白脢,被三個玩家在短短幾天就解決, 有一個十多年來已經無法被測定的蛋白脢,被三個玩家在短短幾天就解決, 或許,這是第一個透過玩電玩而產生的重大的科學進展。 或許,這是第一個透過玩電玩而產生的重大的科學進展。
Last year, on the site of the Twin Towers, the 9/11 memorial opened. It displays the names of the thousands of victims using a beautiful concept called "meaningful adjacency." It places the names next to each other based on their relationships to one another: friends, families, coworkers. When you put it all together, it's quite a computational challenge: 3,500 victims, 1,800 adjacency requests, the importance of the overall physical specifications and the final aesthetics. When first reported by the media, full credit for such a feat was given to an algorithm from the New York City design firm Local Projects. The truth is a bit more nuanced. While an algorithm was used to develop the underlying framework, humans used that framework to design the final result. So in this case, a computer had evaluated millions of possible layouts, managed a complex relational system, and kept track of a very large set of measurements and variables, allowing the humans to focus on design and compositional choices. So the more you look around you, the more you see Licklider's vision everywhere. Whether it's augmented reality in your iPhone or GPS in your car, human-computer symbiosis is making us more capable.
去年,在世貿大樓遺址, 911 紀念館開幕, 它陳列了上千位遇難者的名字 使用一個很美麗概念叫做,「意像連結」 根據遇難者彼此之間的關係把名字放在一起:朋友、家人、同事。 根據遇難者彼此之間的關係把名字放在一起:朋友、家人、同事。 當你把全部都放在一起時,這絕對是一個在計算上的挑戰:三千五百的受難者,一千八百個鄰接的需求。 當你把全部都放在一起時,這絕對是一個在計算上的挑戰:三千五百的受難者,一千八百個鄰接的需求。 最重要的是整體形式的特徵, 以及終極的藝術觀。 當這件事情第一次被媒體報導時,這個功績的榮耀都給了 來自紐約市的名為在地專案(Local Projects)公司的演算法。但事實其實是更令人玩味的。 來自紐約市的名為在地專案(Local Projects)公司的演算法。但事實其實是更令人玩味的。 當一個演算法被用來發展一個相關性架構時, 人類用這個架構去設計出最後的結果。 在這個例子上而言,電腦計算了數百萬種可能的排列, 並管理複雜的關係系統, 並保持追蹤這一套龐大的測量結果與變數, 允許人們去專注於設計和創作上的選擇。 允許人們去專注於設計和創作上的選擇。 所以當你越向四周發覺, 就越能發現力克利德(Licklider)的理念無所不在。 不論iPhone 上面的實境強化,或是車上的定位系統(GPS, Global Positioning System) 人類、電腦 合作讓我們更俱能力。
So if you want to improve human-computer symbiosis, what can you do? You can start by designing the human into the process. Instead of thinking about what a computer will do to solve the problem, design the solution around what the human will do as well. When you do this, you'll quickly realize that you spent all of your time on the interface between man and machine, specifically on designing away the friction in the interaction. In fact, this friction is more important than the power of the man or the power of the machine in determining overall capability. That's why two amateurs with a few laptops handily beat a supercomputer and a grandmaster. What Kasparov calls process is a byproduct of friction. The better the process, the less the friction. And minimizing friction turns out to be the decisive variable.
所以如果你想要改善人類、電腦的合作, 你可以怎麼做? 你可以開始從設計上把人類因子放進過程中。 與其去想如何用電腦解決問題,不如去設計一個人類會怎麼做的解決方案。 與其去想如何用電腦解決問題,不如去設計一個人類會怎麼做的解決方案。 當你這麼做時,你會很快發現, 你將所有的時間花在人和機器的介面上 更精確的在設計上避免在互動中的摩擦。 事實上,這個摩擦在決定全面性能力,比人類個別的力量或著機器個別的力量還要重要。 事實上,這個摩擦在決定全面性能力,比人類個別的力量或著機器個別的力量還要重要。 事實上,這個摩擦在決定全面性能力,比人類個別的力量或著機器個別的力量還要重要。 這就是為什麼兩個業餘選手和幾台筆記型電腦 可以輕鬆的打敗超級電腦和棋聖。 卡斯帕洛夫(Kasparov)稱這種過程為摩擦中的副產品。 過程越順利,摩擦就越少。 減少摩擦卻成為決定性的變項。
Or take another example: big data. Every interaction we have in the world is recorded by an ever growing array of sensors: your phone, your credit card, your computer. The result is big data, and it actually presents us with an opportunity to more deeply understand the human condition. The major emphasis of most approaches to big data focus on, "How do I store this data? How do I search this data? How do I process this data?" These are necessary but insufficient questions. The imperative is not to figure out how to compute, but what to compute. How do you impose human intuition on data at this scale?
或著拿另一個例子:龐大數據。 我們所有在世上的互動,都被一個不斷成長的感應陣列所記錄下來:你的電話、你的信用卡、你的電腦。 我們所有在世上的互動,都被一個不斷成長的感應陣列所記錄下來:你的電話、你的信用卡、你的電腦。 這個結果就是一個龐大數據 而且這在我們面前呈現一個更深入的瞭解人類的狀態機會。 而且這在我們面前呈現一個更深入的瞭解人類的狀態機會。 多數面對龐大數據的方式, 主要著重於「我該如何儲存這些數據,我該如何搜尋這個數據,我該如何處理這個數據?」 主要著重於「我該如何儲存這些數據,我該如何搜尋這個數據,我該如何處理這個數據?」 這些都是必要,但卻不充足的問題。 最迫切的並不是去明白如何計算 而是計算什麼。你如何利用這樣測量尺度下的資料找到人類的直覺? 而是計算什麼。你如何利用這樣測量尺度下的資料找到人類的直覺?
Again, we start by designing the human into the process. When PayPal was first starting as a business, their biggest challenge was not, "How do I send money back and forth online?" It was, "How do I do that without being defrauded by organized crime?" Why so challenging? Because while computers can learn to detect and identify fraud based on patterns, they can't learn to do that based on patterns they've never seen before, and organized crime has a lot in common with this audience: brilliant people, relentlessly resourceful, entrepreneurial spirit — (Laughter) — and one huge and important difference: purpose. And so while computers alone can catch all but the cleverest fraudsters, catching the cleverest is the difference between success and failure.
同樣的,我們一開始就設計人類在這個過程中 當PayPal創業初期,他們最大的挑戰 並不是「我該如何在線上處理金流?」 他們的挑戰是「我該如何做才能排除成為組織化犯罪行為下的詐欺工具?」 為什麼這具有挑戰性?因為當電腦可以依據過去模式學習 如何偵測並辨識詐騙行為, 他們沒辦法根據他們從未見過的模式去學習偵測與辨識, 他們沒辦法根據他們從未見過的模式去學習偵測與辨識, 組織化犯罪擁有很多共通點之一:聰穎的人們, 無情的足智多謀,企業家的精神--(笑聲)-- 而最大的不同在於:目的。 所以雖然單就電腦可以抓到除了聰明者以外所有詐騙者, 但是抓到最聰明的就是成功與失敗不同之處。 但是抓到最聰明的就是成功與失敗不同之處。
There's a whole class of problems like this, ones with adaptive adversaries. They rarely if ever present with a repeatable pattern that's discernable to computers. Instead, there's some inherent component of innovation or disruption, and increasingly these problems are buried in big data.
這裡有一堆像同類型的問題,一個是 會適應的敵人,他們很少表現出 相同的模式,所以無法被電腦察覺。 反之,創新和分裂中有一些固有的因子 而且越來越多這些問題正被龐大的數據埋沒
For example, terrorism. Terrorists are always adapting in minor and major ways to new circumstances, and despite what you might see on TV, these adaptations, and the detection of them, are fundamentally human. Computers don't detect novel patterns and new behaviors, but humans do. Humans, using technology, testing hypotheses, searching for insight by asking machines to do things for them. Osama bin Laden was not caught by artificial intelligence. He was caught by dedicated, resourceful, brilliant people in partnerships with various technologies.
舉例來說,恐怖主義。恐怖份子總是能 能調整自己適應的新環境,而且儘管 你可以會在電視上看到,這些適應, 以及這些察覺都是最基本的人性。 電腦則無法偵測出人意料的模式和新行為, 但是人類可以。人類運用科技,測試假設, 利用機器去幫助他們尋求洞見。 賓拉登並不是靠人工智慧被裝的 他是被全心全意、足智多謀、聰明的人們運用 大量科技器材下被抓的。
As appealing as it might sound, you cannot algorithmically data mine your way to the answer. There is no "Find Terrorist" button, and the more data we integrate from a vast variety of sources across a wide variety of data formats from very disparate systems, the less effective data mining can be. Instead, people will have to look at data and search for insight, and as Licklider foresaw long ago, the key to great results here is the right type of cooperation, and as Kasparov realized, that means minimizing friction at the interface.
雖然這聽起來很有吸引力,你卻不能用 演算數據的方式找到答案 這裡沒有「找到恐怖份子」的按鈕,而且 當我們從大量各種不同來源、系統、規格統整越多的資料, 當我們從大量各種不同來源、系統、規格統整越多的資料, 找尋數據的效率就會降低。 所以,人們會需要看著數據 然後尋找洞見,就像力克利德(Licklider)很久以前的預見, 在此的成功之鑰就是一個正確形式的合作。 就像卡斯帕洛夫(Kasparov)意識到的, 也就是要極小化來自於界面的摩擦
Now this approach makes possible things like combing through all available data from very different sources, identifying key relationships and putting them in one place, something that's been nearly impossible to do before. To some, this has terrifying privacy and civil liberties implications. To others it foretells of an era of greater privacy and civil liberties protections, but privacy and civil liberties are of fundamental importance. That must be acknowledged, and they can't be swept aside, even with the best of intents.
現在這個方式讓許多可能變得有理可尋, 透過大量不同資源中的所有可用的數據 去辨識重要的關係,將他們齊聚一堂, 這是一個以前都無法辦到的事情。 對一些人來說,這會帶來令人害怕的隱私和公民自由的問題。 而對另一些人來說,這預見的是一個有更多 隱私和公民自由保護的世紀, 但隱私和公民自由是最重要的基礎, 需要被認知,而且就算是有最好的目的, 他們不可以被拋再一旁。
So let's explore, through a couple of examples, the impact that technologies built to drive human-computer symbiosis have had in recent time.
所以現在來就來透過一些例子來探討, 近年來,當科技是被製造出來用以驅使人類和電腦的合作時 所帶來的影響力。
In October, 2007, U.S. and coalition forces raided an al Qaeda safe house in the city of Sinjar on the Syrian border of Iraq. They found a treasure trove of documents: 700 biographical sketches of foreign fighters. These foreign fighters had left their families in the Gulf, the Levant and North Africa to join al Qaeda in Iraq. These records were human resource forms. The foreign fighters filled them out as they joined the organization. It turns out that al Qaeda, too, is not without its bureaucracy. (Laughter) They answered questions like, "Who recruited you?" "What's your hometown?" "What occupation do you seek?"
在2007年十月,美國和聯軍部隊 在敘利亞接近伊拉克的邊界新賈爾市突擊蓋達組織 在敘利亞接近伊拉克的邊界新賈爾市突擊蓋達組織。 他們找到了一個珍貴的文件: 700張外國戰士的自傳。 這些戰士們離開波斯灣,地中海東部,非的家人, 在伊拉克加入蓋達組織。 這些記錄是手寫的格式來管理 是這些戰士在他們加入組織時填寫的。 這就發現了其實蓋達組織, 並不是沒有官僚作風。 (笑聲) 他們回答了些問題,像是「誰找你來的?」 「你的家鄉長甚麼樣子?」「你想在這找從事甚麼專長?」
In that last question, a surprising insight was revealed. The vast majority of foreign fighters were seeking to become suicide bombers for martyrdom -- hugely important, since between 2003 and 2007, Iraq had 1,382 suicide bombings, a major source of instability. Analyzing this data was hard. The originals were sheets of paper in Arabic that had to be scanned and translated. The friction in the process did not allow for meaningful results in an operational time frame using humans, PDFs and tenacity alone. The researchers had to lever up their human minds with technology to dive deeper, to explore non-obvious hypotheses, and in fact, insights emerged. Twenty percent of the foreign fighters were from Libya, 50 percent of those from a single town in Libya, hugely important since prior statistics put that figure at three percent. It also helped to hone in on a figure of rising importance in al Qaeda, Abu Yahya al-Libi, a senior cleric in the Libyan Islamic fighting group. In March of 2007, he gave a speech, after which there was a surge in participation amongst Libyan foreign fighters.
在最後一個問題中,揭開了一個令人驚訝的答案。 大部份的外國戰士 都希望能成為殉道的自殺炸彈客-- 這是十分重要的發現,在2003年到2007年間,伊拉克 有1,392個自殺炸彈,是政局不穩定的主要來源 分析這個數據是很困難的。原稿都是待掃描及翻譯,一張張寫著阿拉伯語字的紙。 分析這個數據是很困難的。原稿都是待掃描及翻譯,一張張寫著阿拉伯語字的紙。 這過程中的摩擦並無法於可運用時間內在個別人類、PDF文件以及韌性中得到有意義的結果。 這過程中的摩擦並無法於可運用時間內在個別人類、PDF文件以及韌性中得到有意義的結果。 這過程中的摩擦並無法於可運用時間內在個別人類、PDF文件以及韌性中得到有意義的結果。 研究人員們需要提升他們的思想, 與科技合作去尋找更深,去探測不這麼明顯的 假設。而且事實上,見解已經出現了。 20%的外國戰士來自利比亞 這其中的百分之50是從利比亞的同一個小鎮出來的 這十分重要,因為之前的統計只有算出 百分之三。這也幫助我們去磨鍊 找尋蓋達組織、阿布.葉海亞.禮畢, 一個資深的利比亞伊斯蘭教的神職人員。 在2007年三月,他給了從利比亞踴躍加入的外國戰士一個演說, 在2007年三月,他給了從利比亞踴躍加入的外國戰士們一個演說。
Perhaps most clever of all, though, and least obvious, by flipping the data on its head, the researchers were able to deeply explore the coordination networks in Syria that were ultimately responsible for receiving and transporting the foreign fighters to the border. These were networks of mercenaries, not ideologues, who were in the coordination business for profit. For example, they charged Saudi foreign fighters substantially more than Libyans, money that would have otherwise gone to al Qaeda. Perhaps the adversary would disrupt their own network if they knew they cheating would-be jihadists.
或許這當中最聰明,也最不明顯的, 是改變數據的使用方式,研究者們便可以 更深的探討敘利亞的協調網路 是如何負責處理 到邊界接收和運輸外國戰士。 這些是網路的傭兵,而不是思想啟蒙家 那些為了利潤而進行商業協調。 舉例來說,他們指控沙烏地阿拉伯外國戰士 大大超過利比亞,否則那些錢 就會到蓋達組織手上 或許敵人會在自己的網路自亂陣腳, 如果他們最後了解自己欺騙那些準聖戰者。
In January, 2010, a devastating 7.0 earthquake struck Haiti, third deadliest earthquake of all time, left one million people, 10 percent of the population, homeless. One seemingly small aspect of the overall relief effort became increasingly important as the delivery of food and water started rolling. January and February are the dry months in Haiti, yet many of the camps had developed standing water. The only institution with detailed knowledge of Haiti's floodplains had been leveled in the earthquake, leadership inside. So the question is, which camps are at risk, how many people are in these camps, what's the timeline for flooding, and given very limited resources and infrastructure, how do we prioritize the relocation? The data was incredibly disparate. The U.S. Army had detailed knowledge for only a small section of the country. There was data online from a 2006 environmental risk conference, other geospatial data, none of it integrated. The human goal here was to identify camps for relocation based on priority need. The computer had to integrate a vast amount of geospacial information, social media data and relief organization information to answer this question. By implementing a superior process, what was otherwise a task for 40 people over three months became a simple job for three people in 40 hours,
在2010年一月,一個毀滅性的芮氏規模7地震襲擊海地, 三個最致命的地震,使得一百萬人, 近百分之十的人口,無家可歸。 一個看起來在整體救災貢獻不是很大的工作 例如分配食物和供水 變成更加重要的事。 一月和二月是海地的旱季 然而很多的營地都有蓄水, 這裡唯一擁有海地洪泛區細節的 一個機構,已在地震 中被夷為平地 所以問題會是,哪一個營地目前是處在危險中 目前有多少人在這些營地, 洪水氾濫的時辰,還有在非常有限的資源和 基礎設施下,我們該如何決定搬遷的優先順序? 這個數據是難以置信的歧異。美國陸軍只有 一小部份地區的細節資料。 當時還有從2006年環境風險座談會的網路數據, 其他的地理空間資料,沒有一項可以用來整合使用。 人類要辨識營地的目地,是為了能 根據他們的需要的優先順序進行搬遷。 電腦需要統整各式各樣且大量的地理資訊、社群媒體的數據和救災團體的資料去回答這個問題。 電腦需要統整各式各樣且大量的地理資訊、社群媒體的數據和救災團體的資料去回答這個問題。 電腦需要統整各式各樣且大量的地理資訊、社群媒體的數據和救災團體的資料去回答這個問題。 透過執行這個更佳的處理過程,原本需要40 個人花費超過三個月的時間的任務,只要三個人花40個小時的簡單的工作, 透過執行這個更佳的處理過程,原本需要40 個人花費超過三個月的時間的任務,只要三個人花40個小時的簡單的工作, 透過執行這個更佳的處理過程,原本需要40 個人花費超過三個月的時間的任務,只要三個人花40個小時的簡單的工作,
all victories for human-computer symbiosis.
這都是人類跟電腦合作的勝利。
We're more than 50 years into Licklider's vision for the future, and the data suggests that we should be quite excited about tackling this century's hardest problems, man and machine in cooperation together. Thank you. (Applause) (Applause)
我們比力克利德(Licklider)的對未來的願景 還超前了50年,而數據成果也建議我們在人類和機器一起合作下應該要,對於能夠解決這世紀最困難的問題而感到興奮。 還超前了50年,而數據成果也建議我們在人類和機器一起合作下應該要,對於能夠解決這世紀最困難的問題而感到興奮。 還超前了50年,而數據成果也建議我們在人類和機器一起合作下應該要,對於能夠解決這世紀最困難的問題而感到興奮。 謝謝。(掌聲) (掌聲)