谷歌们的人工智能雄心：让A.I.创造A.I.
Mengxin Li
They are a dream of researchers but perhaps a nightmare for highly skilled computer programmers: artificially intelligent machines that can build other artificially intelligent machines.
这是研究人员梦寐以求的东西，但对有高技能的计算机程序员来说可能是场噩梦：能构建其他人工智能机器的人工智能机器。
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)的主要工程师之一杰夫·迪安(Jeff Dean)最近在硅谷和中国的演讲中，专门提到一个名为AutoML的谷歌项目。ML是机器学习(machine learning)的缩写，指的是通过分析数据来学习如何完成某种特定任务的计算机算法。依次而论，AutoML指的是一种学习如何构建其他机器学习算法的机器学习算法。
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.
有了这个东西，谷歌也许很快能找到一种构建人工智能的技术，它能在构建人工智能系统时在一定程度上不需要人类，许多人认为这是技术产业的未来。
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.
这个项目是一个更大努力的一部分，谷歌想把最新、最棒的人工智能技术推广给越来越多的公司和软件开发人员使用。
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.
技术行业正在做出各种各样的承诺，从能够识别面孔的智能手机应用，到能够自主驾驶的汽车。但据某种估计，全世界只有1万人拥有构建驱动这种新型人工智能的复杂且有时神秘的数学算法所需的教育背景、经验和才能。
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和微软(Microsoft)在内的世界最大技术企业每年支付给人工智能专家的报酬有时高达数百万美元，这些企业基本上垄断了这个难得人才的市场。人才短缺问题不会很快消失，因为掌握这些技能需要多年的工作经验。
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.
但这个行业不愿等待。这些公司正在开发各种各样的工具，让任何企业都能更容易地构建自己的人工智能软件，包括图像和语音识别服务、以及在线聊天机器人这样的东西。
“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.”
“我们所走的道路与计算机科学在每个新技术出来时所经历的一样，”微软副总裁约瑟夫·斯洛什(Joseph Sirosh)说，微软最近推出了一个帮助程序员构建深度神经网络的工具，这是一种推动人工智能领域最新进展的计算机算法。“我们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.
这不是利他主义。迪安等研究人员认为，如果更多的人和企业都来研究人工智能的话，那将会推动迪安等人自己的研究。与此同时，谷歌、亚马逊(Amazon)和微软等公司也在斯洛什描述的趋势中看到了赚大钱的机会。所有这些公司都在推销能帮助其他企业和开发人员构建人工智能的云计算服务。
“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.”
“对这些服务真的有需求，”码特(Matt Scott)说，他是提供类似服务的中国初创企业码隆科技的联合创始人和首席技术官。“这些工具还不能满足所有的需求。”
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.
这很可能是谷歌为AutoML设想的未来，公司正在不停地为项目的进展报喜。谷歌首席执行官桑达尔·皮查伊(Sundar Pichai)上个月推出一款新的Android智能手机时曾吹嘘了AutoML项目。
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.
迪安说，谷歌的这个项目最终将能帮助其他公司构建自己的人工智能系统，即使它们没有广泛的相关专业知识。他估计，目前有能力构建人工智能系统的公司不超过几千家，但更多的公司有所需的数据。
“We want to go from thousands of organizations solving machine learning problems to millions,” he said.
“我们希望看到能解决机器学习问题的公司从几千家变为几百万家，”他说。
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.
谷歌正大力投资云计算服务，这是一种帮助其他企业搭建并运行软件的服务。在谷歌看来，这是他们未来几年的主要经济发展引擎之一。在将相当一部分世界顶级人工智能研究人员引入公司后，谷歌是有能力快速启动这个引擎的。
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.
神经网络正迅速促进着人工智能的发展。不需徒手搭建图像识别服务或语言翻译应用软件，不需一行一行地写代码，工程师们可以更快地编出自身有学习能力的算法。
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，通过分析以往的大量技术支持通话声音，一个机器学习算法可以学会辨识语音。
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.”
但构建神经网络与搭建网站或某个普通智能手机应用不同，它需要大量数学技能，尽可能多的试错，以及一定程度的直觉。独立机器学习实验室Element AI的首席执行官让-弗朗索瓦·加涅(Jean-François Gagné)将这个过程称为“一种新型计算机编程”。
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.
在构建神经网络时，研究人员会在大型的机器网络间进行数十次甚至几百次试验，以测试一个算法学习如图像识别、语言翻译等任务的效果。然后，他们会对算法的特定部分进行反复调整，直到找到可行的办法。有人称之为“黑魔法”，因为研究人员觉得无法解释他们为什么要进行一些特定的调整。
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.
但谷歌正尝试在AutoML身上将这一过程自动化。这是在构建能分析其他算法开发的算法，学习哪种方法行得通，哪种不行，最终将学会更有效的机器学习。谷歌表示，AutoML现在可以构建的图像对象识别算法有时比完全由人类专家构建的服务更精准。
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.
谷歌该项目的研究人员之一巴里·佐夫(Barret Zoph)相信，同样的方法最终也会对其他任务有效，如语音识别或机器翻译。
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.”
这可能不太好理解。但它属于人工智能研究一个重要趋势的一部分。专家将其称为“学会学习”或“元学习”。
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.
许多人相信，这种方法会极大地加快网络及现实中人工智能的发展。在加州大学伯克利分校(University of California, Berkeley)，研究人员正在构建能够让机器人根据之前所学进而学习新任务的技术。
“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.”
“大体上说，计算机将会代替我们创造算法，”伯克利教授彼得·阿比尔(Pieter Abbeel)说。“计算机构建的算法可以非常快地解决很多很多问题——至少希望是这样。”
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.
同时这也让更多的人和企业能够去构建人工智能。这些方法不能完全取代人工智能研究人员。专家——比如谷歌的专家——仍需做大部分重要的设计工作。但它要实现的是以少数专家的工作来帮助其他更多的人搭建自己的软件。
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.
卡内基梅隆大学研究与AutoML相似技术的研究员雷纳托·内格尼奥(Renato Negrinho)说，这不是今天的现实，但会是未来几年的现实。“迟早会来的，”他说。
作者：Cade Metz
本文最初发表于2017年11月5日。
翻译：纽约时报中文网
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