Inside the Allen Institute for Artificial Intelligence, known as AI2, everything is white .
艾倫人工智慧研究所（簡稱AI2）裡面，所有東西都是白色。
"The brilliant white was a conscious choice meant to evoke experimental science — think 'white lab coat,' " said Oren Etzioni, the director of the new institute, which the Microsoft co-founder Paul Allen launched last year as a sibling of the Allen Institute for Brain Science, his effort to map the human brain.
這間新研究所的所長艾特齊昂尼說：「我們刻意選擇亮白色，給人實驗科學的感覺，想想『白色實驗袍』。」微軟共同創辦人艾倫去年創立這間研究所，是艾倫腦研究所的姊妹所，後者是他為繪製人腦圖譜而設。
Yet the futuristic surroundings offer a paradoxical note: AI2 is an effort to advance artificial intelligence while simultaneously reaching back into the field's past.
不過，未來感十足的環境卻有著弔詭的調性：AI2是為了發展人工智慧，卻同時回到這個領域的過去。
While Silicon Valley looks to fashionable techniques like neural networks and machine learning that have rapidly advanced the state of the art, Dr. Etzioni remains a practitioner of a modern version of what used to be known as Gofai, for good old-fashioned artificial intelligence.
矽谷指望著時髦的技術，像是已促使最尖端科技快速推進的神經網路和機器學習，而艾特齊昂尼博士仍是過去稱為「老派人工智慧」（Gofai）現代版的實踐者。
The reference goes back to the earliest days of the field in the 1950s and '60s, when artificial-intelligence researchers were confident they could model human intelligence using symbolic systems — logic embedded in software programs, running on powerful computers. Then in the late 1980s, an early wave of commercial artificial-intelligence companies failed. The field was seen as a failure and went into eclipse.
這要回溯到1950和60年代這個領域的發展初期，當時人工智慧研究人員有信心，他們能利用符號系統模擬人類的智慧，也就是把邏輯寫入軟體程式，在功能強大的電腦上跑。接著到了80年代末，最早一波商用人工智慧公司全倒了。這個領域公認失敗，從而沒落。
In recent years, however, A.I. has come roaring back as speech recognition, machine vision and self-driving cars have made progress with powerful computers, cheap sensors and machine- learning techniques.
不過，近年拜功能強大的電腦、便宜的感應器和機器學習科技之賜，讓語音辨識、機器視覺和自動駕駛汽車大有進展，人工智慧又大張旗鼓捲土重來。
But the debate over how to reach genuine artificial intelligence has not ended, and Dr. Etzioni and Mr. Allen are betting that their path is more pragmatic. The power of the new techniques is not disputed, but there is a growing debate over whether they can take the field to human-level capabilities by themselves. "Think of it as Sherlock Holmes versus Spider-Man," said Jerry Kaplan, a visiting lecturer at Stanford University , comparing Holmes's deductive powers with the irrational "spider sense" that tingles at the base of Spider-Man's skull and alerts him to danger.
但有關如何達到真正人工智慧的辯論還沒結束，艾特齊昂尼和艾倫看好他們走的這條路更實際。新技術的力量不容爭辯，至於人工智慧能否僅憑這些新技術便達到人類能力的水平，辯論卻日益激烈。史丹福大學訪問學者卡普蘭說：「這好比福爾摩斯對上蜘蛛人。」他比較福爾摩斯的演繹能力與蜘蛛人不合邏輯的「蜘蛛感覺」，蜘蛛人頭裡會產生刺痛，警告他有危險。
Dr. Etzioni says that the artificial- intelligence field has made incremental advances in areas like vision and speech, but that we have gotten no closer to the larger goal of true human-level systems.
艾特齊昂尼說，人工智慧領域在視覺和語音等方面逐漸進步，但我們距離真正人類水平系統這個更大的目標，並沒有更近。
"Driverless cars are a great thing," he said, but added that the field had given rise to "bad A.I., like the N.S.A. is using it or Facebook is using it to track you."
他說：「自動駕駛汽車是了不起的東西。」但他接著說，這個領域助長了「壞的人工智慧，像是國家安全局或臉書利用它追蹤你」。
He says both he and Mr. Allen believe that technology cannot be separated from its social and economic consequences. They have added a social mission to the project that they call "artificial intelligence for the common good."
他說，他和艾倫都認為科技與它的社會和經濟影響不可分割。他們賦予這計畫一項社會任務，稱之為「合乎公益的人工智慧」。
The success or failure of the project will ultimately hinge on whether Dr. Etzioni can create a new synthesis of artificial intelligence, weaving together powerful machine-learning tools with traditional logic-oriented software. The current fad for big data, of which machine learning is a major component, has significant limits. "If you step back a little and say we want to do A.I., then you will realize that A.I. needs knowledge, reasoning and explanation," he said. "My argument is that big data has made great progress in limited areas."
計畫成敗最終要看艾特齊昂尼能否創造新的人工智慧綜合體，把功能強大的機器學習工具和邏輯導向的傳統軟體結合起來。現今對巨量資訊的狂熱有其重大限制，而機器學習為其要件。他說：「你若稍稍後退，說我們要做人工智慧，將會明白人工智慧需要知識、推理和解釋。我的看法是，巨量資料在受限制的範疇內有很大進展。」
Even Watson, the brainy IBM computer whose intelligence the company wants to apply in complex applications like medical diagnoses and automated call centers with interactive speech recognition, will soon reach fundamental limits, he argues.
他認為就連IBM頭腦很棒的電腦「華生」很快都會達到基本的極限。IBM要把華生的智慧應用在複雜的用途，像是醫療診斷和使用互動語音辨識的自動客服中心。
"I really don't want a system that can't explain itself to be my doctor," he said. "I can just imagine sitting there with Dr. Watson and the program saying, 'Well, we need to remove a kidney, Mr. Etzioni,' and I'm like, 'What?!' and they respond, 'Well, we have a lot of variables and a lot of data, and that's just what the model says.' "
他說：「我真的不要一個不能把自己解釋清楚的系統來當我的醫生，我可以想像與華生醫生坐在哪兒，華生說，『這個嘛，我們需要割掉一個腎』，我說，『什麼？！』他們回答，『這個嘛，我們有很多變數和很多資料，而這就是這個模型的結論。』」
Some technology experts argue that self-aware computing machines are now on the horizon. "As for A.I. progress, we're mostly haggling about a few decades," said Hans Moravec, a leading roboticist who is the chief scientist of Seegrid Corporation, a maker of autonomous vehicles for warehouse applications. "I'm content to simply watch it play out, trying to do my part. I do want fully autonomous robots as soon as possible, to begin visiting the rest of the universe."
一些科技專家認為，有自覺意識的計算機就快出現了。「至於人工智慧的進展，我們大致落後幾十年」，頂尖機器人專家、倉庫用自動車製造商Seegrid公司首席科學家莫拉維奇說。「單單看著它發展，並盡一己之力，我就心滿意足了。我真的想要全自動的機器人，愈快愈好，開始探訪宇宙的其他地方。」
Mr. Allen and Dr. Etzioni are not so optimistic. Both are skeptical of claims that we may be only years away from machines that think in any human sense.
艾倫和艾特齊昂尼沒這麼樂觀。有人聲稱能像人類一樣思考的機器或許再過幾年就會出現，但他們兩人都不相信這種說法。
"Full A.I., " Mr. Allen wrote in an email , "is probably a hundred years away (or more). In reality, we are only beginning to grasp how deep intelligence works."
艾倫在電郵中說：「完全的人工智慧可能還要百年（或更久）。實則我們才剛開始理解智慧的運作有多深奧。」
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