本周二，蘋果（Apple）人工智能研究部門的主任魯斯蘭·薩拉赫丁諾夫探討了這項技術(shù)的部分局限性。不過，在這次《麻省理工科技評論》（MIT Technology Review）的會議上，對于他所在的神秘公司會如何將人工智能應(yīng)用于Siri等產(chǎn)品，他回避了討論。

去年10月加入蘋果的薩拉赫丁諾夫表示，他對于人工智能中強化學(xué)習(xí)這個領(lǐng)域尤其有興趣。利用這種方法，研究人員可以教電腦反復(fù)采取各種舉動，找出最優(yōu)的解決方案。例如，谷歌（Google）就用強化學(xué)習(xí)幫助其數(shù)據(jù)中心的計算機找到了最優(yōu)的冷卻和運轉(zhuǎn)配置，從而提高了能源的利用效率。

薩拉赫丁諾夫還是卡內(nèi)基梅隆大學(xué)（Carnegie Mellon）的助理教授。他說，這所大學(xué)最近在利用強化學(xué)習(xí)，訓(xùn)練計算機玩一款20世紀90年代的電子游戲《毀滅戰(zhàn)士》（Doom）。計算機很快就學(xué)會了準確射擊外星人，還發(fā)現(xiàn)回避動作可以躲開敵軍的火力。然而，這些專業(yè)的《毀滅戰(zhàn)士》計算機系統(tǒng)不太善于記憶迷宮場景等，這導(dǎo)致它們無法規(guī)劃和設(shè)計策略。

薩拉赫丁諾夫的研究目標之一，就是開發(fā)能記住《毀滅戰(zhàn)士》虛擬迷宮及幾個特殊參照點，從而定位特定巨塔的人工智能軟件。這款軟件在游戲中會先查看火炬是紅色還是綠色，因為火炬顏色的不同，意味著需要定位的巨塔顏色不同。

最后，軟件學(xué)會了通過迷宮，抵達正確的巨塔。如果它走錯了，也會原路返回迷宮找到正確的那個。薩拉赫丁諾夫表示，尤其值得注意的是，軟件每次看到巨塔，都能回憶起開始看到的火炬顏色。

然而，他也表示，這種人工智能需要“長期的訓(xùn)練時間”，還要求強大的運算能力，因此很難大批量生產(chǎn)。他說：“目前來看，它還太脆弱了。”

薩拉赫丁諾夫另一個想要探索的領(lǐng)域，就是讓人工智能軟件更快地通過“少數(shù)案例和經(jīng)歷”進行學(xué)習(xí)。盡管他沒有明說，不過他的想法應(yīng)該能幫助蘋果在“用更少時間做出更好產(chǎn)品”的競爭中取得優(yōu)勢。

一些人工智能的專家和分析師認為，蘋果的人工智能技術(shù)比起谷歌或微軟（Microsoft）等對手要遜色一籌。因為公司有著更嚴格的用戶隱私條款，限制了能夠用于訓(xùn)練計算機的數(shù)據(jù)量。如果蘋果在訓(xùn)練計算機上使用了更少的數(shù)據(jù)，公司或許會在滿足隱私要求的情況下，用媲美競爭對手的速度改進軟件。（財富中文網(wǎng)）

作者：Jonathan Vanian

譯者：嚴匡正

On Tuesday, Apple’s director of AI research, Ruslan Salakhutdinov, discussed some of those limitations. However, he steered clear during his talk at an MIT Technology Review conference of how his secretive company incorporates AI into its products like Siri.

Salakhutdinov, who joined Apple in October, said he is particularly interested in a type of AI known as reinforcement learning, which researchers use to teach computers to repeatedly take different actions to figure out the best possible result. Google (goog, +0.17%), for example, used reinforcement learning to help its computers find the best possible cooling and operating configurations in its data centers, thus making then more energy efficient.

Researchers at Carnegie Mellon, where Salakhutdinov is also an associate professor, recently used reinforcement learning to train computers to play the 1990's era video game Doom, Salakhutdinov explained. Computers learned to quickly and accurately shoot aliens while also discovering that ducking helps with avoiding enemy fire. However, these expert Doom computer systems are not very good at remembering things like the maze's layouts, which keeps them from planning and building strategies, he said.

Part of Salakhutdinov’s research involves creating AI-powered software that memorizes the layouts of virtual mazes in Doom and points of references in order to locate specific towers. During the game, the software first spots what's either a red or green torch, with the color of the torch corresponding to the color of the tower it needs to locate.

Eventually, the software learned to navigate the maze to reach the correct tower. When it discovered the wrong tower, the software backtracked through the maze to find the right one. What was especially noteworthy was that the software was able to recall the color of the torch each time it spotted a tower, he explained.

However, Salakhutdinov said this type of AI software takes “a long time to train” and that it requires enormous amounts of computing power, which makes it difficult to build at large scale. “Right now it’s very brittle,” Salakhutdinov said.

Another area Salakhutdinov wants to explore is teaching AI software to learn more quickly from “few examples and few experiences.” Although he did not mention it, his idea would benefit Apple in its race to create better products in less time.

Some AI experts and analysts believe Apple's AI technologies are inferior to competitors like Google or Microsoft because o聽f the company's stricter user privacy rules, which limits the amount of data it can use to train its computers. If Apple used less data for computer training, it could perhaps satisfy its privacy requirements while still improving its software as quickly as rivals.