谷歌們的人工智能雄心：讓A.I.創(chuàng)造A.I.
這是研究人員夢寐以求的東西，但對有高技能的計算機程序員來說可能是場噩夢：能構建其他人工智能機器的人工智能機器。
With recent speeches in both Silicon Valley and China, Jeff Dean, one of Google’s leading engineers, spotlighted a Google project called AutoML. ML is short for machine learning, referring to computer algorithms that can learn to perform particular tasks on their own by analyzing data. AutoML, in turn, is a machine-learning algorithm that learns to build other machine-learning algorithms.
谷歌(Google)的主要工程師之一杰夫·迪安(Jeff Dean)最近在硅谷和中國的演講中，專門提到一個名為AutoML的谷歌項目。ML是機器學習(machine learning)的縮寫，指的是通過分析數(shù)據(jù)來學習如何完成某種特定任務的計算機算法。依次而論，AutoML指的是一種學習如何構建其他機器學習算法的機器學習算法。
With it, Google may soon find a way to create A.I. technology that can partly take the humans out of building the A.I. systems that many believe are the future of the technology industry.
有了這個東西，谷歌也許很快能找到一種構建人工智能的技術，它能在構建人工智能系統(tǒng)時在一定程度上不需要人類，許多人認為這是技術產業(yè)的未來。
The project is part of a much larger effort to bring the latest and greatest A.I. techniques to a wider collection of companies and software developers.
這個項目是一個更大努力的一部分，谷歌想把最新、最棒的人工智能技術推廣給越來越多的公司和軟件開發(fā)人員使用。
The tech industry is promising everything from smartphone apps that can recognize faces to cars that can drive on their own. But by some estimates, only 10,000 people worldwide have the education, experience and talent needed to build the complex and sometimes mysterious mathematical algorithms that will drive this new breed of artificial intelligence.
技術行業(yè)正在做出各種各樣的承諾，從能夠識別面孔的智能手機應用，到能夠自主駕駛的汽車。但據(jù)某種估計，全世界只有1萬人擁有構建驅動這種新型人工智能的復雜且有時神秘的數(shù)學算法所需的教育背景、經驗和才能。
The world’s largest tech businesses, including Google, Facebook and Microsoft, sometimes pay millions of dollars a year to A.I. experts, effectively cornering the market for this hard-to-find talent. The shortage isn’t going away anytime soon, just because mastering these skills takes years of work.
包括谷歌、Facebook和微軟(Microsoft)在內的世界最大技術企業(yè)每年支付給人工智能專家的報酬有時高達數(shù)百萬美元，這些企業(yè)基本上壟斷了這個難得人才的市場。人才短缺問題不會很快消失，因為掌握這些技能需要多年的工作經驗。
The industry is not willing to wait. Companies are developing all sorts of tools that will make it easier for any operation to build its own A.I. software, including things like image and speech recognition services and online chatbots.
但這個行業(yè)不愿等待。這些公司正在開發(fā)各種各樣的工具，讓任何企業(yè)都能更容易地構建自己的人工智能軟件，包括圖像和語音識別服務、以及在線聊天機器人這樣的東西。
“We are following the same path that computer science has followed with every new type of technology,” said Joseph Sirosh, a vice president at Microsoft, which recently unveiled a tool to help coders build deep neural networks, a type of computer algorithm that is driving much of the recent progress in the A.I. field. “We are eliminating a lot of the heavy lifting.”
“我們所走的道路與計算機科學在每個新技術出來時所經歷的一樣，”微軟副總裁約瑟夫·斯洛什(Joseph Sirosh)說，微軟最近推出了一個幫助程序員構建深度神經網絡的工具，這是一種推動人工智能領域最新進展的計算機算法。“我們正在消除大量的繁重工作。”
This is not altruism. Researchers like Mr. Dean believe that if more people and companies are working on artificial intelligence, it will propel their own research. At the same time, companies like Google, Amazon and Microsoft see serious money in the trend that Mr. Sirosh described. All of them are selling cloud-computing services that can help other businesses and developers build A.I.
這不是利他主義。迪安等研究人員認為，如果更多的人和企業(yè)都來研究人工智能的話，那將會推動迪安等人自己的研究。與此同時，谷歌、亞馬遜(Amazon)和微軟等公司也在斯洛什描述的趨勢中看到了賺大錢的機會。所有這些公司都在推銷能幫助其他企業(yè)和開發(fā)人員構建人工智能的云計算服務。
“There is real demand for this,” said Matt Scott, a co-founder and the chief technical officer of Malong, a start-up in China that offers similar services. “And the tools are not yet satisfying all the demand.”
“對這些服務真的有需求，”碼特(Matt Scott)說，他是提供類似服務的中國初創(chuàng)企業(yè)碼隆科技的聯(lián)合創(chuàng)始人和首席技術官。“這些工具還不能滿足所有的需求。”
This is most likely what Google has in mind for AutoML, as the company continues to hail the project’s progress. Google’s chief executive, Sundar Pichai, boasted about AutoML last month while unveiling a new Android smartphone.
這很可能是谷歌為AutoML設想的未來，公司正在不停地為項目的進展報喜。谷歌首席執(zhí)行官桑達爾·皮查伊(Sundar Pichai)上個月推出一款新的Android智能手機時曾吹噓了AutoML項目。
Eventually, the Google project will help companies build systems with artificial intelligence even if they don’t have extensive expertise, Mr. Dean said. Today, he estimated, no more than a few thousand companies have the right talent for building A.I., but many more have the necessary data.
迪安說，谷歌的這個項目最終將能幫助其他公司構建自己的人工智能系統(tǒng)，即使它們沒有廣泛的相關專業(yè)知識。他估計，目前有能力構建人工智能系統(tǒng)的公司不超過幾千家，但更多的公司有所需的數(shù)據(jù)。
“We want to go from thousands of organizations solving machine learning problems to millions,” he said.
“我們希望看到能解決機器學習問題的公司從幾千家變?yōu)閹装偃f家，”他說。
Google is investing heavily in cloud-computing services — services that help other businesses build and run software — which it expects to be one of its primary economic engines in the years to come. And after snapping up such a large portion of the world’s top A.I researchers, it has a means of jump-starting this engine.
谷歌正大力投資云計算服務，這是一種幫助其他企業(yè)搭建并運行軟件的服務。在谷歌看來，這是他們未來幾年的主要經濟發(fā)展引擎之一。在將相當一部分世界頂級人工智能研究人員引入公司后，谷歌是有能力快速啟動這個引擎的。
Neural networks are rapidly accelerating the development of A.I. Rather than building an image-recognition service or a language translation app by hand, one line of code at a time, engineers can much more quickly build an algorithm that learns tasks on its own.
神經網絡正迅速促進著人工智能的發(fā)展。不需徒手搭建圖像識別服務或語言翻譯應用軟件，不需一行一行地寫代碼，工程師們可以更快地編出自身有學習能力的算法。
By analyzing the sounds in a vast collection of old technical support calls, for instance, a machine-learning algorithm can learn to recognize spoken words.
比如，通過分析以往的大量技術支持通話聲音，一個機器學習算法可以學會辨識語音。
But building a neural network is not like building a website or some run-of-the-mill smartphone app. It requires significant math skills, extreme trial and error, and a fair amount of intuition. Jean-François Gagné, the chief executive of an independent machine-learning lab called Element AI, refers to the process as “a new kind of computer programming.”
但構建神經網絡與搭建網站或某個普通智能手機應用不同，它需要大量數(shù)學技能，盡可能多的試錯，以及一定程度的直覺。獨立機器學習實驗室Element AI的首席執(zhí)行官讓-弗朗索瓦·加涅(Jean-François Gagné)將這個過程稱為“一種新型計算機編程”。
In building a neural network, researchers run dozens or even hundreds of experiments across a vast network of machines, testing how well an algorithm can learn a task like recognizing an image or translating from one language to another. Then they adjust particular parts of the algorithm over and over again, until they settle on something that works. Some call it a “dark art,” just because researchers find it difficult to explain why they make particular adjustments.
在構建神經網絡時，研究人員會在大型的機器網絡間進行數(shù)十次甚至幾百次試驗，以測試一個算法學習如圖像識別、語言翻譯等任務的效果。然后，他們會對算法的特定部分進行反復調整，直到找到可行的辦法。有人稱之為“黑魔法”，因為研究人員覺得無法解釋他們?yōu)槭裁匆M行一些特定的調整。
But with AutoML, Google is trying to automate this process. It is building algorithms that analyze the development of other algorithms, learning which methods are successful and which are not. Eventually, they learn to build more effective machine learning. Google said AutoML could now build algorithms that, in some cases, identified objects in photos more accurately than services built solely by human experts.
但谷歌正嘗試在AutoML身上將這一過程自動化。這是在構建能分析其他算法開發(fā)的算法，學習哪種方法行得通，哪種不行，最終將學會更有效的機器學習。谷歌表示，AutoML現(xiàn)在可以構建的圖像對象識別算法有時比完全由人類專家構建的服務更精準。
Barret Zoph, one of the Google researchers behind the project, believes that the same method will eventually work well for other tasks, like speech recognition or machine translation.
谷歌該項目的研究人員之一巴里·佐夫(Barret Zoph)相信，同樣的方法最終也會對其他任務有效，如語音識別或機器翻譯。
This is not always an easy thing to wrap your head around. But it is part of a significant trend in A.I. research. Experts call it “learning to learn” or “meta-learning.”
這可能不太好理解。但它屬于人工智能研究一個重要趨勢的一部分。專家將其稱為“學會學習”或“元學習”。
Many believe such methods will significantly accelerate the progress of A.I. in both the online and physical worlds. At the University of California, Berkeley, researchers are building techniques that could allow robots to learn new tasks based on what they have learned in the past.
許多人相信，這種方法會極大地加快網絡及現(xiàn)實中人工智能的發(fā)展。在加州大學伯克利分校(University of California, Berkeley)，研究人員正在構建能夠讓機器人根據(jù)之前所學進而學習新任務的技術。
“Computers are going to invent the algorithms for us, essentially,” said a Berkeley professor, Pieter Abbeel. “Algorithms invented by computers can solve many, many problems very quickly — at least that is the hope.”
“大體上說，計算機將會代替我們創(chuàng)造算法，”伯克利教授彼得·阿比爾(Pieter Abbeel)說。“計算機構建的算法可以非常快地解決很多很多問題——至少希望是這樣。”
This is also a way of expanding the number of people and businesses that can build artificial intelligence. These methods will not replace A.I. researchers entirely. Experts, like those at Google, must still do much of the important design work. But the belief is that the work of a few experts can help many others build their own software.
同時這也讓更多的人和企業(yè)能夠去構建人工智能。這些方法不能完全取代人工智能研究人員。專家——比如谷歌的專家——仍需做大部分重要的設計工作。但它要實現(xiàn)的是以少數(shù)專家的工作來幫助其他更多的人搭建自己的軟件。
Renato Negrinho, a researcher at Carnegie Mellon University who is exploring technology similar to AutoML, said this was not a reality today but should be in the years to come. “It is just a matter of when,” he said.
卡內基梅隆大學研究與AutoML相似技術的研究員雷納托·內格尼奧(Renato Negrinho)說，這不是今天的現(xiàn)實，但會是未來幾年的現(xiàn)實。“遲早會來的，”他說。
