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在人工智能下一个重大突破可能来自美国

Where A.I.'s Next Big Breakthrough May Come From
在人工智能下一个重大突破可能来自美国
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2019-09-11 13:01
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在人工智能下一个重大突破可能来自美国

A pioneer in artificial intelligence says conventional companies can still distinguish themselves in A.I. despite worries that tech giants like Google and Amazon have already won.

人工智能领域的一位先驱表示,尽管人们担心谷歌和亚马逊(Amazon)等科技巨头已经胜出,但传统公司仍能在人工智能领域脱颖而出。

Andrew Ng, a prominent Silicon Valley executive and investor who previously led some of the biggest A.I. projects at Google and its Chinese rival Baidu, says the next wave of A.I. will be in industries in which the tech giants aren’t firmly rooted. Think manufacturing, agriculture, and healthcare.

硅谷知名高管、投资者吴恩达(Andrew Ng)曾在谷歌及其中国竞争对手百度领导过一些规模最大的人工智能项目。他说,下一波人工智能将出现在这些科技巨头尚未站稳脚跟的行业。考虑下制造业、农业和医疗。

Ng is a bit biased considering that his latest venture, Landing AI, helps traditional companies adopt A.I. But he makes a compelling argument that established companies still have a chance.

考虑到吴恩达的最新企业“着陆人工智能”(Landing AI)帮助传统企业采用人工智能,他的观点有点偏颇,但他提出了一个令人信服的论点,即老牌企业仍有机会。

Speaking at TechCrunch’s business technology conference in San Francisco last week, Ng likened the current state of A.I. to the Internet's rise in the 1990s. Companies like Apple, Microsoft, and FedEx were not Internet natives, he explained, but they were able to become “Internet companies” by creating new businesses that depended on the Web.

上周在旧金山举行的TechCrunch商业技术会议上,吴恩达将目前人工智能的状况比作上世纪90年代互联网的崛起。他解释说,像苹果、微软和联邦快递这样的公司并不是互联网原住民,但他们能够通过创造依赖网络的新业务成为“互联网公司”。

For instance, Apple was primarily a computer maker, but it eventually created a huge business out of its Internet-driven app store. These companies did more than merely create websites and apps and then call it a day.

例如,苹果最初是一家电脑制造商,但它最终从互联网驱动的应用程序商店中创造了一项巨大的业务。这些公司所做的不仅仅是创建完网站和应用程序就收工。

Likewise, traditional companies that haven’t embedded A.I. into their businesses still have time to do so. It just won’t be as simple as buying a cloud software service “where you swipe your credit card and you use it and now your company is A.I.-enabled,” Ng said.

同样,没有将人工智能嵌入业务的传统公司仍有时间这么做。它不会像购买云软件服务那么简单,“你刷了信用卡就能用它,然后现在你的公司也就启用了人工智能,”吴恩达说。

Instead, executives at traditional businesses must think hard about how they can apply deep learning, a key component of artificial intelligence, to their specific needs. For them, Ng has a few tips. For instance, agriculture companies could affix sensors to their farming equipment to collect data about their fields and then use A.I. techniques to analyze that data to obtain better crop yields.

相反,传统企业的高管们必须认真考虑如何将深度学习(人工智能的一个关键组成部分)应用于他们的特定需求。关于他们,吴恩达有一些建议。例如,农业公司可以在他们的农业设备上安装传感器,收集关于他们农田的数据,然后使用人工智能技术分析这些数据,以获得更好的农作物产量。

The challenge is that current deep learning techniques, many of which were created by the tech giants, only work well with enormous quantities of data. Non-tech companies, like agricultural businesses, may have to develop their own A.I. techniques that rely on only small amounts of farm data, Ng said.

挑战在于,目前的深度学习技术(其中许多是由科技巨头发明的)只能在海量数据下很好地工作。N吴恩达说,像农业企业这样的非技术公司可能必须开发自己的人工智能技术,这些技术只依赖于少量的农业数据。

But if agricultural companies create neural networks—the foundational software used for data training— that learn from small amounts of data, it would be a huge breakthrough. This could level the playing field between the A.I.-powered tech giants and conventional businesses.

但是,如果农业企业能够神经网络—数据培训的基础软件—从少量数据中学习,这将是一个巨大的突破。这可以在人工智能科技巨头和传统企业之间建立一个公平的竞争环境。

“One of the myths we tell in Silicon Valley is that whenever there is disruptive technology, the startups always win,” Ng says. “That’s not true.”

吴恩达说:“我们在硅谷流传的一个神话是,只要有颠覆性技术,初创企业总是赢家。”“这不是真的。”

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A.I. IN THE NEWS

出现在新闻中的人工智能

The Pentagon and A.I. ethics. The Pentagon is looking to hire an ethics expert who can help the Defense Department navigate some of A.I.’s most pressing ethical concerns, trade publication Defense Systems reported. The news comes amid employee protest at companies like Google over the potential military uses of A.I. and the company’s role in selling the government powerful, data-crunching technology.

五角大楼和人工智能伦理。五角大楼正在寻找一名道德专家,帮助国防部处理一些人工智能问题贸易出版物《国防系统》(Defense Systems)报道称,这是美国最紧迫的道德问题。这一消息传出之际,谷歌等公司的员工正在抗/议人工智能的潜在军事用途,以及该公司在销售强大的数据处理技术政府方面所扮演的角色。

The Department of Energy’s A.I. office. The U.S. Department of Energy created the DOE Artificial Intelligence and Technology Office, which is intended to coordinate the department’s A.I. projects as part of the White House’s national A.I. strategy. Energy Sec. Rick Perry said in statement that the new office would “concentrate our existing efforts while also facilitating partnerships and access to federal data, models and high performance computing resources for America’s AI researchers.”

能源部的人工智能办公室。美国能源部成立了能源部人工智能与技术办公室,旨在协调能源部的人工智能项目,这是白宫国家人工智能战略的一部分。能源部长里克·佩里在一份声明中表示,新办公室将“集中我们现有的努力,同时促进合作关系,为美国人工智能研究人员提供联邦数据、模型和高性能计算资源。”

Singapore’s A.I. dreams. Singapore is trying to cultivate an A.I. technology scene and remain a neutral A.I. player between China and the U.S., Bloomberg News reported. The island city-state’s government is investing $500 million on A.I.-related projects through 2020, and the nation is now home to A.I. research offices of Alibaba and Salesforce.

新加坡的人工智能梦想。据彭博新闻社(Bloomberg News)报道,新加坡正在努力打造一个人工智能技术领域,并在中美之间保持中立,成为人工智能参与者。这个岛国政府到2020年在人工智能领域投资5亿美元,中国将成为阿里巴巴Salesforce人工智能研究办公室的所在地。

Academic A.I. brain drain. The New York Times reported on a study showing the impact on universities and the startups they produce when A.I. professors leave their full-time academic positions to work at corporations. The study “focused on the start-up economy, showing that departures led to fewer student start-ups,” the Times reported, noting that “experts are split on whether a decline in the start-up economy will harm the progress of A.I.”

学术人工智能人才流失。《纽/约/时/报》(The New York Times)报道了一项研究,该研究显示了当人工智能教授离开全职学术岗位去公司工作时,他们对大学和初创企业的影响。《纽/约/时/报》报道称,这项研究“关注的是创业经济,研究显示离职导致学生创业数量减少”,并指出“对于创业经济下滑是否会损害人工智能的进步,专家们意见不一。”

DEEP LEARNING DOUBTS

深度学习的疑虑

Computer science experts Gary Marcus and Ernest Davis write in The New York Times about the limitations of deep learning technologies and why other A.I. approaches are important. The two write: “In particular, we need to stop building computer systems that merely get better and better at detecting statistical patterns in data sets — often using an approach known as deep learning — and start building computer systems that from the moment of their assembly innately grasp three basic concepts: time, space and causality.”

计算机科学专家加里·马库斯(Gary Marcus)和欧内斯特·戴维斯(Ernest Davis)在《纽/约/时/报》撰文,阐述了深度学习技术的局限性,以及为什么其他人工智能方法很重要。两人写道:“特别是,我们需要停止构建那些仅仅越来越擅长检测数据集中的统计模式的计算机系统——通常使用一种被称为深度学习的方法——并开始构建那些从组装时就天生掌握时间、空间与因果关系三个基本概念的计算机系统。”

EYE ON A.I. TALENT

关注人工智能人才

Online music service Spotify hired Tony Jebara as president of engineering for personalization and to lead its machine-learning strategies. Jebara, also a Columbia University computer science professor, was previously a machine learning director at Netflix.

在线音乐服务Spotify聘请托尼•杰巴拉(Tony Jebara)担任个性化工程总裁,并负责其机器学习策略。杰巴拉也是哥伦比亚大学(Columbia University)计算机科学教授,曾在Netflix担任机器学习主管。

EYE ON A.I. RESEARCH

关注人工智能研究

Deep learning’s gender problem. The Pew Research Center published a study about the difficulties deep-learning systems have identifying people’s genders based on their photos. The study showed that gender-classification systems generally work better when they are trained with a diverse set of photos representing multiple age-groups and ethnicities.

深度学习的性别问题。皮尤研究中心(Pew Research Center)发表了一项关于深度学习系统根据人们的照片识别性别存在困难的研究。研究表明,当他们接受一组代表不同年龄层和种族的不同照片训练时,性别分类系统通常会工作得更好。

In some cases, however, the researchers found that gender-classification systems can occasionally work well when trained on less diverse datasets, which the Pew Research team found surprising and confusing. 

然而,在某些情况下,研究人员发现,性别分类系统有时可以很好地工作,当训练较少的不同数据集时,皮尤研究小组的发现令人惊讶和困惑。

A.I.-aided drug discovery. Researchers from biotechnology firm Insilico Medicine published a paper in the Nature Biotechnology journal about using A.I. techniques to significantly decrease the amount of time it takes to create molecules useful for drug discovery. The researchers’ technology used a combination of reinforcement learning—a type of A.I. that learns through many trials—and so-called generative adversarial networks, which can be used to create realistic, but fake photos, among other tasks.

人工智能协助发现毒品。生物技术公司Insilico 医药的研究人员在《自然生物技术》杂志上发表了一篇论文,介绍了如何利用人工智能技术显著减少制造出对发现毒品有用的的分子所需的时间。研究人员的技术将强化学习(一种通过多次试验学习的人工智能)和所谓的生成性对抗性网络(这一网络可以用来创建真实但虚假的照片等任务)结合起来。

FORTUNE ON A.I.

人工智能带来的财富。

被深度伪造视频吓到了,脸书发起了一场检测视频的竞赛——杰里米·卡恩

大多数美国人不信任使用面部识别技术的公司 – 乔纳森·多尼

深度山寨应用Zao让你成为电影明星。但这也引发了人们对隐私的巨大担忧 – 艾丽莎· 纽科姆

BRAIN FOOD

健脑食品

Making A.I. safe for the U.S. and the rest of the world. Researchers in the U.S. and China must work together on A.I. to ensure that the technology is safe, writes Matt Sheehan, a fellow at the Paulson Institute’s MacroPolo think tank, in Bloomberg. Sheehan is concerned that competition between the two countries in A.I. could lead to U.S. lawmakers severing ties between U.S. and Chinese A.I. researchers, who sometimes collaborate and communicate with each other during A.I. conferences and on research projects. Doing so, he writes, “threatens to create a dangerous knowledge vacuum on AI safety precisely when we need smart, strategic cooperation between scientists to mitigate these risks. In this case, engagement will make the U.S. far safer than isolation.”

让人工智能在美国和世界其他地方都是安全的。保尔森研究所的宏观经济学智囊团的研究员马特•希恩(Matt Sheehan)在彭博(Bloomberg)撰文称,美国和中国的研究人员必须在人工智能方面合作,以确保该技术是安全的。希恩担心,中美两国在人工智能领域的竞争可能会导致美国议员切断中美人工智能研究人员之间的联系。他写道,这样做“有可能在我们需要科学家之间进行明智的战略合作以减轻这些风险的时候,在人工智能安全方面制造一个危险的知识真空”。在这种情况下,相比孤立,接触将使美国更为安全。”

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