警惕人工智能热：AI离“下一个大事件”还远着呢

人工智能在过去几年中取得了巨大的进步，但对它当前的功能，也有些宣传过度的嫌疑。

这是上周五旧金山的一个专家座谈会得出的结论。该座谈会是由美国计算机学会在颁发第50届图灵奖期间举办的，该奖项主要奖励的是在计算机科学领域做出杰出贡献者。

加州大学伯克利分校的机器学习专家、计算机科学教授迈克尔·乔丹表示，当前人们对所谓“聊天机器人”的功能宣传得有些过头了。很多此类软件使用的都是一种叫“深度学习”的技术，人们会用海量的会话数据对它进行“培训”，使它知道如何与真人进行互动。

尽管好几家大型科技企业和创业公司都推出了能像真人一样回答问题的聊天机器人，不过乔丹认为，由于人类的语言极其复杂，机器人凭借深度学习等现有的技术，依然无法完全掌握我们的语言。这些机器人本质上其实是在耍“社交手腕”，他们可以针对特定语境做出宽泛的回应，但是它们“说不出关于现实世界的任何真实情况”。

乔丹表示：“我们进入了一个机器学习被炒得火热的时代。”机器学习虽然有改变整个经济的面貌的潜力，但“我们还没到那个时候。”

会上，谷歌云机器学习首席科学家、斯坦福大学教授李飞飞表示：“我们生活在人工智能的一个最激动人心、也是被炒得最火热的时代。”李飞飞参与发起了ImageNet计算机视觉挑战赛，这次挑战赛又让人工智能重新火了一把。参赛者要运用机器学习技术，在照片中识别诸如猫之类的物体。

李飞飞表示，虽然大家都在谈论ImageNet的成功，“但我们很少讨论失败。”她还强调，为了制造出能像真人一样“看”东西的计算机，研究人员们付出了难以想象的艰辛。

不过李飞飞仍然信心十足地表示，目前人工智能领域已经取得了不少具有里程碑意义的成就，最终它们将为我们带来更多的突破，其影响会触及诸如医疗保健等每一个行业。她表示：“我们正在进入人工智能的一个新阶段。”

OpenAI公司是一家由伊隆·马斯克注资的人工智能研究公司。该公司的研究总监伊利娅·苏特斯科娃指出，深度学习技术要想取得更多突破，首先取决于计算机硬件技术的持续发展，比如英伟达的GPU等等，如此才能确保人工智能系统以比以往更快的速度数据海量数据。深度学习技术将继续与计算机硬件一道飞速发展，目前看来还没有丝毫减缓的迹象。

苏特斯科娃表示：“计算能力是深度学习技术的‘氧气’。”（财富中文网）

译者：朴成奎

Artificial intelligence has made great strides in the past few years, but it’s also generated much hype over its current capabilities.

That’s one takeaway from a Friday panel in San Francisco involving leading AI experts hosted by the Association for Computing Machinery for its 50th annual Turing Award for advancements in computer science.

Michael Jordan, a machine learning expert and computer science professor at University of California, Berkeley, said there is “way too much hype” regarding the capabilities of so-called chat bots. Many of these software programs use an AI technique called deep learning in which they are “trained” on massive amounts of conversation data so that they learn to interact with people.

But despite several big tech companies and new startups promising powerful chat bots that speak like humans when prodded, Jordan believes the complexity of human language it too difficult for bots to master with modern techniques like deep learning. These bots essentially perform parlor tricks in which they respond with comments that are loosely related to a particular conversation, but they “can’t say anything true about the real world.”

“We are in era of enormous hype of deep learning,” said Jordan. Deep learning has the potential to change the economy, he added, but “we are not there yet."

Also in the panel, Fei-Fei Li, Google’s (goog, +0.89%) machine learning cloud chief and Stanford University Professor, said “We are living in one of the most exciting and hyped eras of AI.” Li helped build the ImageNet computer-vision contest, which spurred a renaissance in AI in which researchers applied deep learning to identify objects like cats in photos.

But while everyone talks about ImageNet’s success, “we hardly talk about the failures,” she said, underscoring the hard work researchers have building powerful computers that can “see” like humans.

Still, Li is excited that current AI milestones will eventually lead to more breakthroughs that will touch every single industry, like healthcare. “We are entering a new phase in AI,” she said.

What will help usher more breakthroughs in deep learning will be the continuing advancements in powerful computing hardware, like Nvidia's GPUs that make it possible to crunch tremendous amounts of data faster than ever, explained Ilya Sutskever, the research director of Elon Musk-backed AI research group OpenAI. Deep learning will keep booming in tandem with advancements in computing hardware that shows no signs of slowing down.

"Compute has been the oxygen of deep learning," Sutskever said.