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人工智能行业速览——为何硬件对AI的未来如此重要

Jonathan Vanian 2019年05月20日

用好人工智能的关键不仅仅在于软件。很多企业很快意识到,人工智能程序运行和训练所依靠的硬件也是至关重要的。

用好人工智能的关键不仅仅在于软件。很多企业很快意识到,人工智能程序运行和训练所依靠的硬件也是至关重要的。

以人工智能领域的领军者谷歌为例,5月初,谷歌在加州山景城召开的年度开发者大会上发布了一系列联网家居产品,它们都与谷歌的语音控制助手Google Assistant相连。

谷歌高管在发布会上介绍道,它的一款新型联网设备Nest Hub Max可以利用摄像头迅速识别出家庭成员中的每个人。这样一来,它就可以对各个用户的要求更好地做出回应,比如按照他的偏好播放歌单或者展示照片等等。

以前,谷歌对人工智能的营销策略是一视同仁地兼容所有安卓(Android)设备。现在,谷歌则越来越多地对自家硬件进行定制,以使人工智能程序运行得更加流畅。

在向《财富》杂志解释谷歌的战略转变时,谷歌智能家居和Nest产品负责人瑞希·钱德拉表示:“我们在软件和硬件上都在发力。”

Facebook和亚马逊也采取了类似战略,他们越来越重视针对自家硬件对人工智能程序进行定制。Facebook和亚马逊都在设计自家的联网设备和数据中心芯片,以满足各自的机器学习任务。

谷歌、Facebook和亚马逊的策略,对那些试图将人工智能整合到业务中的企业有何借鉴意义?人工智能的使用,不仅仅是简单地将数字输入一个花哨的软件,然后坐等天上掉钱。实际上,那些行业领先的人工智能公司不仅部署了大量员工开发软件,同时也在打造人工智能程序所基于的硬件。有些公司还构建了自己的数据中心来承担部分任务。比如沃尔玛最近就在纽约州莱维敦的一家充满未来感的门店里构建了自己的数据中心,而不是使用云服务。

人工智能是一项庞大而昂贵的工程,如果有人说只搞搞软件就好了,千万别信他的忽悠。

人工智能新闻速览

苹果与SAP在人工智能领域开展合作。根据一项扩大合作协议,德国商业软件巨头SAP正在对iPhone和iPad的应用开发工具进行升级,以添加对苹果Core ML人工智能工具的支持。这项协议似乎与2018年苹果跟IBM的协议大同小异,根据去年的协议,IBM将它的Watson数据处理服务与苹果的Core ML技术进行了连接。

感受思科的声音。思科已经将其MindMeld数字助手和语音控制技术放在了开源平台上,使其他公司和开发者可以对其进行修改和改进。思科在2017年以1.25亿美元收购了MindMeld,以加强它的工作协作产品。

爱立信在加拿大设立人工智能研究中心。网络巨头爱立信在加拿大蒙特利尔建立了一个人工智能研究中心,计划聘请30名数据科学家、机器学习工程师和软件开发人员从事人工智能研发。蒙特利尔培养了许多优秀的人工智能领域的人才,因此,谷歌、Facebook、微软等其他几家大型科技公司也在蒙特利尔设有人工智能研究实验室。

Facebook“标记”用户个人数据。据路透社报道,Facebook通过来自IT公司Wipro以及其他一些咨询机构的印度合同工人,用手工方式给用户照片等内容打上了标签,以训练该公司的人工智能系统。Facebook对路透社表示,公司会告诉用户,根据它的数据政策,公司之所以使用用户的数据,是为了“改进用户体验”。不过路透社的报道也指出,用户“并没有选择不让他们的个人数据被标记的机会”。

你真的懂你的数据吗?

据科技新闻网站TechRepublic报道,如果企业不能正确理解或追踪公司的所有数据,就不可能搞好机器学习。教育科技公司GoGuardian的数据科学负责人瑞恩·约翰逊对《纽约时报》表示,在数据处理上,“很多公司的做法都是本末倒置。”

人工智能招聘速览

无人驾驶创业公司Ghost Locomotion近日任命大卫·珀迪为该公司首席科学家。珀迪曾任Uber安全数据科学团队的高级数据科学经理。

数据分析公司Altery近日任命艾伦·雅各布森担任首席数据与分析官。雅各布森曾任福特公司全球分析总监。

英国皇家邮政任命凯特·詹姆斯为其数据科学部门的新负责人。他的前任本·迪亚斯现为易捷航空公司的数据科学总监。

人工智能研究速览

人工智能成为人口普查工具。来自于斯坦福大学、达特茅斯学院和世界银行的研究人员近日发表了一篇关于利用深度学习技术估算印度农村人口的论文。研究人员利用在印度农村地区上空拍摄的卫星图像对他们的神经网络进行训练,并表示他们的系统要比传统的人口普查方式表现得更好。而且“如果有更高分辨率的图像可用,它可能还有进一步提升的空间。”

人工智能的隐私问题。来自于加州大学伯克利分校、杜克大学和中国零售业巨头京东集团的研究人员发表了一篇论文,探讨了强化学习技术可能带来的隐私方面的风险。所谓强化学习,是指计算机通过反复试验和试错进行学习的过程。研究人员发现,在所谓的模拟“训练环境”中(比如在一个机器人可以自主认路和行走的仓库中),人工智能程序可以学习到某些对强化学习至关重要的细节。

烧脑新闻

人工智能成了与“气候变化”相提并论的大问题。英国《金融时报》撰文称,在各国为成为全球人工智能领域的领导者激烈竞争的过程中,各国政府及企业很可能会忽视人工智能的某些道德困境,从而导致严重后果,比如加重现有的社会偏见。对此,深度学习技术的“教父”乔舒亚·本吉奥建议,人工智能也应该有一个“国际秩序”,包括由政府来制定行业规范。他表示:“就像应对气候变化问题一样,我们必须要让那些不按规则办事、忽视全球利益的国家蒙羞。”(财富中文网)

译者:朴成奎

Using artificial intelligence isn’t just about software. Companies are quickly realizing that the hardware it runs on and is trained on is also critical.

Take the example of Google, a leader in A.I. At the beginning of May, during its annual developer conference in Mountain View, Calif., the company debuted its new line of Internet-connected home products that are all linked to its voice-controlled Google Assistant.

Executives bragged on stage about how one of the new devices, the Nest Hub Max, can use its camera to immediately recognize individual family members. In that way, the technology can better respond to requests like playing music from someone’s song lists or showing photos that they are more likely to be interested in.

In the past, Google marketed its A.I. as equally compatible with any Android device. But increasingly, it’s tailoring its A.I. to its own hardware so that it operates more smoothly.

“The pendulum swings both ways,” Rishi Chandra, Google’s head of Home and Nest products told Fortune, explaining Google’s philosophical shift.

Facebook and Amazon are following a similar strategy to Google in their growing focus on customizing A.I to their hardware. They’re designing their own Internet-connected devices and data center chips tailored for their own machine-learning tasks.

What’s the lesson here for businesses that are trying to incorporate A.I. into their operations? Using A.I. isn’t as simple as feeding numbers into fancy software and waiting for a result that will lead to lots of profits. In fact, leading A.I. companies have deployed legions of employees to work on complex software while also fine tuning the hardware that runs and trains it. Some companies are also building their own data centers to handle some of the work, as Walmart recently did inside its futuristic store in Levittown, N.Y., rather than using a cloud service.

It’s all a huge and expensive undertaking. Don’t believe anyone who says differently.

EYE ON A.I. NEWS

Apple and SAP are A.I. buddies. Under an expanded partnership, German business software giant SAP is updating its software development toolkit for building iPhone and iPad apps to include support for Apple’s Core ML A.I. tools. The agreement appears to be similar to 2018 deal between Apple and IBM that linked IBM’s Watson data crunching service with Apple’s Core ML technology.

Come play with Cisco’s voice. Cisco has made its MindMeld digital assistant and voice technology available in open source so other companies and developers can modify and improve it. Cisco bought MindMeld in 2017 for $125 million to enhance its work-collaboration products.

Ericsson plants A.I. flag in Canada. Networking giant Ericsson debuted an A.I. research hub in Montreal and plans to hire 30 data scientists, machine learning engineers, and other software developers to work in the new unit. Several other big tech companies like Google, Facebook, and Microsoft also have A.I. research labs in Montreal, a leading city for deep learning talent.

Facebook has to “label” data somehow. Facebook uses Indian contract workers from IT firm Wipro, among other consulting groups, to hand-label people’s photos and other content in order to train its A.I. systems, Reuters reported. Facebook told the news service that it tells its users in its data policy that the company uses people’s information to “improve their experience.” But, the report noted that users are “not offered the chance to opt out of their data being labeled.”

DO YOU REALLY KNOW YOUR DATA?

Companies can’t do machine learning well if they don’t understand or properly track all of their corporate data, according to tech news site TechRepublic. Ryan Johnson, the data science head of education tech company GoGuardian, told the publication that when it comes to data crunching, “A lot of companies are putting the cart before the horse there.”

EYE ON A.I. HIRES

Ghost Locomotion, a startup specializing in self-driving car technology, has named David Purdy as chief scientist. Purdy was previously a senior data science manager for Uber’s safety data science team.

Data analytics company Alteryx hired Alan Jacobson as chief data and analytics officer. Jacobson was formerly the director of global analytics for Ford.

United Kingdom postal-service company Royal Mail has chosen Kat James as its new head of data science. James replaces Ben Dias, who is now the data science director of airline company EasyJet.

EYE ON A.I. RESEARCH

A.I. as a census tool. Researchers from Stanford University, Dartmouth College, and the World Bank published a paper about using deep learning to estimate the population in rural areas of India. The researchers trained their neural networks on satellite imagery taken from above rural villages and said that their systems performed better than traditional efforts, and that there “may still have room for improvement if images with higher resolution are available.”

A.I.’s privacy problems. Researchers from organizations like U.C. Berkeley, Duke University, and Chinese retail giant JD.com, Inc. published a paper exploring potential privacy problems that can occur with reinforcement learning—in which computers learn through trial and error. The researchers discovered that they could learn certain details within so-called simulated “training environments” (like a warehouse floor where a robot learned to navigate around) that are crucial for reinforcement learning.

BRAIN FOOD

A.I. as a “climate change” issue. The Financial Times looks at how countries racing to be the world’s leader in A.I. could result in governments and companies failing to consider some of A.I.’s ethical dilemmas, such as increasing existing societal biases. Deep learning “godfather” Yoshua Bengio suggests an “international order” for A.I. that would involve governments creating the norms. “Just like with climate change, we have to stigmatise (sic) countries which don’t want to play by the rules necessary for the benefit of the whole planet,” he told the publication.

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