This $2 billion AI start-up aims to teach factory robots to think


Bloomberg | May 19, 2018, 11:34 IST

Japan's Preferred Networks Inc. has only one publicly available product, a whimsical application that uses artificial intelligence to automate the coloring of manga cartoons.

日本的Preferred Networks Inc.公司只有一款公开发售的产品,一个异想天开的应用程序,通过人工智能技术为漫画图稿上色。


Yet the four-year-old firm has become Japan's most valuable startup, with a venture capital funding that priced it at more than $2 billion, according to people familiar with the matter. Toyota Motor Corp., its biggest backer, handed over $110 million on a bet its algorithms will help them compete with Google in driverless cars. Last February, Prime Minister Shinzo Abe posed for pictures with the firm's two young founders at his office, where they were awarded a prize for promising new ventures.

What sets Preferred Networks apart from the hundreds of other AI startups is its ties to Japan's manufacturing might. Deep learning algorithms depend on data and the startup is plugging into some of the rarest anywhere. Its deals with Toyota and Fanuc Corp., the world's biggest maker of industrial robots, give it access to the world's top factories. While Google used its search engine to become an AI superpower, and Facebook Inc. mined its social network, Preferred Networks has an opportunity to analyze and potentially improve how just about everything is made.

Preferred Networks与其他AI初创企业的区别在于它与日本制造业的关系。深度学习算法依赖于数据,而Preferred Networks的研究方向则是最稀缺的领域。它与全球最大的工业机器人制造商丰田和发那科公司达成协议,使其有了进入那些世界顶尖工厂的机会。谷歌利用其搜索引擎成为AI霸主,脸谱网则挖掘其社交网络,Preferred Networks则有机会分析及改进所有产品的生产方式。

"There is so much promise for deep learning in manufacturing," said Yutaka Matsuo, a computer scientist at the University of Tokyo and president of Japan Deep Learning Association.
东京大学的计算机科学家、日本深度学习协会会长Yutaka Matsuo表示:“在制造业领域,深度学习有非常好的前景。”

Founders Daisuke Okanohara and Toru Nishikawa met at the University of Tokyo, where they studied computer science in the early 2000s. Okanohara, an engineer whose work on something called context-aware text classification won him a "supercreator' prize from the trade ministry in 2004, directs the firm's research.

Preferred Networks的创始人冈野原大辅和西川徹相识于东京大学,21世纪初,2人同为该校计算机科学专业的学生。作为一名工程师,冈野原大辅的工作主要是关于情境感知文本分类的研究,这也让他在2004年赢得了日本经济产业省颁发的“超级创造者”奖项。目前,他领导着Preferred Networks的研究工作。

Nishikawa is the company's president and pitchman. A cherubic 35-year-old, he says his fascination with computers started in elementary school. By 8th grade, he was lugging a primitive laptop the size of a car battery with him wherever he went. He told his teachers it was for note-taking, but he was actually writing programs.


Nishikawa spoke at his Tokyo headquarters, a drab collection of meeting rooms in an old office building more fitting of a down-on-its-luck insurance company. A handful of industrial robots, used for experiments, share the space with 140 or so engineers. The firm also has one of Japan's most powerful supercomputers, though its exact location is secret.

Preferred Networks位于东京的总部更像是一家保险公司,在一栋老旧的办公楼里,还有单调乏味的会议室。一些用于试验的工业机器人与约140名工程师共享这片空间。该公司还拥有全日本最快的超级电脑之一,尽管它被藏在什么地方是个秘密。

"People are always coming up with beautiful new office plans for us," Nishikawa said with a laugh. "But if I'm going to spend the money, I'd rather buy more computing clusters."

“有人总想为我们设计更漂亮的办公室,” 西川徹笑着说,“有这笔钱,我还不如买电脑呢。”

In separate interviews, the founders talked about everything from their childhoods to their AI ambitions. One thing they wouldn't discuss in detail was work for partners such as Toyota or Fanuc, for whom they've become like an outsourced AI research arm.


The idea of founding a business came while Nishikawa and Okanohara were working part-time at a biotech startup, writing software for genome sequencing. Their first venture, staffed with university friends, built a machine learning platform that could parse text faster than any application could generate it.


Then in 2012, scientific breakthroughs in deep learning made it possible for computers to reliably do things like understand speech and recognize objects, opening new realms where crunching data at speed would be useful. Nishikawa and Okanohara started Preferred Networks in 2014 and decided to focus on making industrial machines smarter, a shrewd decision because Japan still makes cutting-edge manufacturing equipment, and deep-pocketed AI superpowers like Google and Facebook haven't carved up the territory.

接着,2012年,深度学习领域出现一系列突破,使计算机能够可靠地完成诸如理解语音和识别对象的工作,打开了以速度计算数据的新领域。2014年,冈野原和西川创办Preferred Networks,决定开发更智能的工业机器人。这是一个明智的决定,因为在制造业领域,日本仍能制造出最尖端的设备,而谷歌和Facebook这样的AI大户尚未进军该领域。

"It's an area where a Japanese company stands a chance of winning," said Matsuo, the head of Japan Deep Learning Association.

日本深度学习协会会长Yutaka Matsuo表示:“这是一个日本公司有机会获胜的领域。”

One of the first people to buy into their vision was Fanuc Chairman Yoshiharu Inaba. A famously guarded businessman and a brilliant engineer who'd himself invented important tools for car manufacturing, Inaba agreed to meet Nishikawa and Okanohara in early 2015.


A one-hour conversation convinced him to give the two computer scientists $9 million, along with access to some of his most closely kept commercial secrets--vast streams of data generated by the thousands of robots on his factory lines.


"I felt we were on the same wavelength," Inaba explained in a rare interview.


Toyota followed Inaba's commitment four months later with $10 million of its own, adding another $100 million last August. Manufacturing powerhouse Hitachi Ltd., megabank Mizuho Financial Group Inc., and trading house Mitsui Co. became backers in December.

四个月后,丰田紧跟着发那科的步伐投资了1000万美金,去年8月丰田又投资了一亿美金。此外,制造业传统豪强日立、银行巨头瑞穗金融以及三井贸易公司都在12月成为了Preferred Networks的投资人。

At the Las Vegas Consumer Electronics Show in 2016, a simple demonstration using toy cars showed some of what their technology can do. The demo had a half-dozen miniature Toyota Priuses set loose on an obstacle course. At first, the cars could hardly move without colliding. But after two hours of trial and error, they were zipping around as if they had professional drivers inside.

在2016年拉斯维加斯的国际消费类电子产品博览会上,Preferred Networks用玩具汽车对自家的技术做了一次简单的展示。他们用几台微缩的丰田普锐斯穿过场上的障碍物。一开始,玩具车撞来撞去,举步维艰,但经过两个小时的持续试错之后,它们就能畅通无阻地在障碍物中穿梭了。

No human programmer had written instructions for them. Instead, they'd derived their own rules from experience, and the process was sped up by sharing it across a network (like learning from all of your friend's mistakes if your friends could tell you everything).


At an expo in Japan a few months later, another demo showed how the tech might one day be used to turn factory robots into something closer to skilled craftsmen. Programming a Fanuc bin-picking robot to grab items out of a tangled mass might take a human engineer several days. Nishikawa and Okanohara showed that machines could teach themselves overnight. Working together, a team of eight could master the task in an hour. If thousands -- or millions -- were linked together, the learning would be exponentially faster.

几个月后在日本的一次展会上,他们又展示了自己的技术如何让工厂内的机器人更接近优秀的人类技工。给一个发那科机器人编程,让它能顺利在一团乱麻之中抓出指定的物品,可能要花费一个人类工程师几天时间。而Preferred Networks的展示中,八台以团队形式进行工作的机器人能在一小时之内掌握这项技能——如果数千台,甚至数百万台机器人被联结在一起,学习速度将会呈指数级提升。

"It takes 10 years to train a skilled machinist, and that knowledge can't just be downloaded to another person" Fanuc's Inaba explained. "But once you have a robot expert, you can multiply it infinitely."

“训练一个出色的技工要花费十年,而且,他到时所拥有的知识也不能被下载到另一个人身上。” 发那科的稻叶解释道,“但要是你有了一位机器人专家,你就能让那些知识无限地膨胀。”

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