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商业 - 科技

人工智能需要什么?同理心和监管

McKenna Moore 2018年12月18日

随着此项技术变得越发普及,应该把监管和同理心摆在首要位置。

AI革命就要来了。

采用机器学习这一关键人工智能技术的公司数量已经超过大家的想象。随着此项技术变得越发普及,应该把监管和同理心摆在首要位置。

上周三,在加州拉古纳尼格召开的《财富》2018年最具影响力下一代女性峰会上,情感人工智能公司Affectiva的联合创始人兼首席执行官拉娜·埃尔·卡柳比表示,在技术领域里,情商和智商同样重要。她指出,考虑到人与技术互动的频率以及技术在人们生活中越来越大的影响,把同理心融入技术之中很重要。

埃尔·卡柳比说,要做到这一点,途径之一就是让多元化团队来进行技术开发。对此她举了个例子:中年白人男性在创建和训练面部识别AI时用的都是和他们长相类似的照片,这就意味着这样的AI在面对有色人种女性时往往不能正常发挥作用,甚至根本派不上用场。

“这要归结到设计相关算法的团队身上,如果不是多元化团队,他们就不会考虑这样的AI在戴头巾的女性面前表现如何。”埃尔·卡柳比说:“他们只是解决了自己所知范围内的问题。”

微软AI部门的首席产品负责人纳维里娜·辛格说,她在一个电子商务网站项目中遇到过一个完美的以同理心思维开发技术的例子。这个网站想让印度消费者更方便地购买他们的商品。由于印度的识字率较低,这家公司就为不识字的用户提供了语音转文字功能。他们事先统一行动,用印度各地的方言和文化对AI进行了训练,原因是在不同背景下,用户说话时表达的意图和内容都不一样。IBM沃森部门的客户关系总经理Inhi Cho Suh认为,意图识别是目前AI面临的最主要挑战和机遇之一。

现在,机器学习的另一大焦点是监管。与会者认为,随着机器人和其他相关技术日臻完善,必需有法律来制约这种力量。Suh指出,应通过技术和监管来防止不正当使用。埃尔·卡柳比则强调,大学计算机科学和工程专业学生必须接受伦理教育。

辛格提出用F.A.T.E.这个缩略语来代表开发和监管此类技术时应注意的关键问题。它们是公平(fairness)、负责(accountability)、透明(transparency)和伦理(ethics)。反恐技术公司Moonshot CVE的创始人维迪亚·拉玛林汉姆说,虽然有很多关于AI技术的负面新闻,比如英国政治咨询机构Cambridge Analytica非法获取8700万Facebook用户数据的丑闻,但我们不能让恐惧主导舆论。

她指出:“出台政策的动机不应该是害怕,而是应该在掌握相关知识并了解相关信息后制定政策。”(财富中文网)

译者:Charlie

审校:夏林

The AI revolution is upon us.

Machine learning, one of key artificial intelligence technologies, has already been deployed within more companies than you would expect. As it gains even greater adoption, regulation and empathy should be at the forefront.

Rana el Kaliouby, co-founder and CEO of emotional AI company Affectiva, said at Fortune’s Most Powerful Women Next Gen 2018 in Laguna Niguel, Calif. on last Wednesday that EQ is just as important in technology as IQ. Because of the frequency with which people interact with technology and its growing impact on our lives, it’s important that empathy be built into it, she said.

One way to do that, el Kaliouby said, is to have diverse teams work on the technology. In a example of the problem, she said that middle-aged white men usually create and train face recognition AI using images of people who look like themselves, which means the technology often doesn’t work as well, if at all, on women of color.

“It goes back to the teams designing these algorithms, and if your team isn’t diverse they aren’t going to be thinking about how this will work on a woman wearing hijab,” she said. “You solve for the problems you know.”

Navrina Singh, principal product lead of Microsoft AI, said that a perfect example of building technology with empathy in mind came to her during a project with an e-commerce site that trying to make it easier for customers in India to buy it products. Due to the low literacy rate in the country, the company built speech-to-text functionality for users who couldn’t read. Beforehand, the company made a concerted effort to train its AI in dialects and cultures from all around India, because the intent and meaning of speech varies based on background. Deciphering intent is one of the greatest challenges and opportunities in AI right now, Inhi Cho Suh, general manager of customer engagement at IBM Watson, said.

Regulation is another big topic in machine learning at the moment. With bots and other related technology becoming more sophisticated, laws are necessary to check that power, the panelists agreed. Suh said that technology and regulation should be used to prevent misuse, while el Kaliouby stressed the need for mandatory ethics training for college computer science and engineering majors.

Singh shared the acronym F.A.T.E., which stands for fairness, accountability, transparency and ethics, to sum up the key ideas to keep in mind when creating and regulating this technology. Although there is a lot of bad news about technology, like the Cambridge Analytica scandal, in which a British political firm accessed personal data on up to 87 million Facebook users, we must not let fear guide the debate, said Vidhya Ramalingham, founder of counter-terrorism technology company Moonshot CVE.

“Policy should not be written out of fear, it should be written in an educated and informed manner,” she said.

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