“隐身”两年后，谷歌前前HRVP公布其新的创业公司
文/Simone Stolzoff
Laszlo Bock是HR世界里的摇滚明星。
在谷歌——一家在“最佳工作场所”名单上常年受到追捧的公司，他管理人力资源达十年 之久；之后他写下了《重新定义团队：谷歌如何工作》成为《纽约时报》(New York Times)打造企业文化的畅销书；然后，他创办了自己的公司Humu。
在两年的大部分时间里，Humu 以隐身模式运作。尽管该公司很少错过讨论其使命的机会——“推动人们每天都做最好的自己”——但它几乎没有提供公司实际行动的细节，甚至在5月份宣布已筹集4000万美元风险投资后也是如此。
近日，秘密终于揭晓了。
在一篇博客文章中，Bock描述了Humu的旗舰产品——Nudge Engine。这是一款使用行为科学和机器学习的应用程序，可以在整个工作日为员工提供个性化的“轻推”服务。“轻推”可以简单地提醒您要感谢一位做得很好的同事，或者在会议期间征求一位比较安静的团队成员的意见。
虽然“轻推”这个词可能有一种柔和的含义，但是Humu技术的基础理论来自于硬科学。去年，Richard Thaler教授因其对“ 轻推理论 ”(nudge theory)的研究获得了诺贝尔经济学奖，他的研究证明了小的提示对人们的行为有很大的影响。
“员工之间每天要进行数百万次的交流，从开会到评估，再到开门，不一而足，”Bock说。“在Humu，我们相信每个人都可以尽自己的努力来改变每一个人。”
一个温暖而模糊的推送通知平台可能看起来不像是强大商业模式的基础，但员工敬业度是一门难以追踪和衡量的黑暗艺术之一。工作效率、员工留存和员工士气都与员工在工作中的感受直接相关。
Humu适合更大的教练网络趋势，在这个趋势中，公司实施人工智能工具来指导员工的整个工作日。 Chorus为销售人员提供实时反馈。Textio 让招聘经理知道在他们的岗位上使用的最佳语言。
虽然技术肯定可以帮助人力资源，销售经理和文案编辑的工作，但办公室文化最终都是由人类塑造的。员工们是否会感到被迫遵守机器驱动的建议，最终取决于他们。
以上为AI翻译，观点仅供参考。
原文链接：After two years in stealth mode, the former head of HR at Google reveals his new startup
招聘文本分析创企Textio，获800万美元A轮融资来源：猎云网（编译：竹子）
Textio，一家分析特定情景中的词汇和语言的创企，今天宣布完成了由Emergence Capital领投的800万美元A轮融资。Cowboy Ventures、Bloomberg Beta和Upside Partnership也参与了此次融资。
Textio的第一款工具瞄准的是人才并购领域如招聘。创始人Snyder发现，某些特定的词汇和设计对应聘者更有吸引力，于是这些预测分析就被吸纳进了Textio的服务里。Textio可以分析一个公司的职务说明、绩效考核与其它备案，并判断这些文案是否可以为公司取得最佳效果。
该软件利用人工智能技术扫描招聘文本信息，然后向公司建议进行调整，以提高该公司吸引能力强的应聘者的机会。比如，有重点句的职位招聘总是比没有的更吸引求职者。Textio的软件还会建议各公司引进更多样化的应聘者。例如女性职场人士通常不会参与办公室内斗，或通常不会从事代码类的工作。
Textio成立于去年秋天，它的两位创始人 Kieran Snyder 和 Jensen Harris 分别是微软和亚马逊的前员工，Kieran Snyder之前致力于科技公司中性别歧视的研究，同时曾在微软、亚马逊任职语言学家，Jensen Harris曾在微软工作过16年。Textio创办后不到5个月，就在今年2月份拿到了150万美元的融资。
Textio主要以三个方面来衡量相关的科技短语：一是申请包含该短语岗位的应聘者人数；二是满足该词语要求技能的应聘者在全部应聘者中的百分比；第三是工作招聘发出后多久能够招到相关的人才。
虽然目前它的用处在于职位招聘，但很明显，这些技术可以被用到其他很多方面，比如邮件、简历以及其他各类信息。如果技术运行良好，理论上它可以为各类文档搭建分数库，这或许也是吸引投资人的地方。
Textio还有其他有价值的过人之处吗？大概就是它的客户了吧。目前使用Textio服务的企业有Twitter、Atlassian、Starbucks、Square和Microsoft等等。自然语言处理技术有广阔的应用领域，这又是对投资人的另一大强烈吸引。
Snyder表示，Textio目前可以识别出超过6万句短语词组，而这一数据还在持续增长。它会研究词汇是以何种方式组合在一起，比如词组中动词的密度和其他语法相关的特性。基于以上种种，最后给出评定分数。
当然，Textio也有不少潜在的竞争对手，诸如IBM Watson理论上也能分析文本并给出类似的结果。不过Snyder表示他们的优势在于专注的内容领域更具体。
Textio, A Startup That Analyzes Text Performance, Raises $8M
Textio CEO Kieran Snyder took a quantitative approach to how language worked in her linguistics studies. And when she and her co-founder Jensen Harris were leaving Microsoft to start a new company, it was only natural that it would be centered around language in some way.
That’s how Textio, a startup that analyzes text for how well words and phrases perform in certain scenarios, was born. The company today said it raised $8 million in a financing round led by Emergence Capital. Cowboy Ventures, Bloomberg Beta, and Upside Partnership also participated in the financing round.
“We had this premise that word processing in text hadn’t been disrupted in a while, from command line to GUI,” CEO Kieran Snyder said. “We had the internet come along, it was about social and sharing, and we think that AI and the set of related technologies is the next big disruptor of text. If you know the performance of a document before it’s ever published then you can fix it before it’s published.”
Textio’s first tool looks at talent acquisition documents — like job postings — to determine how well they will perform among candidates. Certain words and layouts attract more candidates than others, Snyder found, and those predictive analytics are baked into the service. For example, Textio shows that job postings with bullet points tend to perform better than job postings without them.
Right now it’s used for talent acquisition documents, but it’s pretty easy to see that the technology can be applied to documents that include common phrases — such as email, resumes, or other kinds of messages. If the technology works, it can theoretically begin building up scores for those kinds of documents, which is likely what attracted investors to the product and the team.
Another reason it might be so valuable to investors? Its customers. Already Textio is being used by companies like Twitter, Atlassian, Starbucks, Square and Microsoft. Natural Language Processing technology has very broad applications if done right, which makes it an attractive bet for many investors.
Textio recognizes more than 60,000 phrases with its predictive technology, Snyder said, and that data set is changing constantly as it continues to operate. It looks at how words are put together — such as how verb dense a phrase is — and at other syntax-related properties the document may have. All that put together results in a score for the document, based on how likely it is to succeed in whatever the writer set out to do.
Given who’s likely using Textio, it’s important that it feels easy to use — hence the highlighting and dropdown boxes rather than readouts. Snyder said, at its core, Textio can’t feel like a statistics tool, and that’s probably because the kinds of people using it might not always be NLP experts.
Of course, there are potential competitors in the space when it comes to natural language processing. There are tools like IBM Watson that can analyze text and, in theory, pull off a similar result. But Snyder says Textio’s results will be better because they are content-specific — like in the case of talent-acquisition documents.
Source:TC
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