用神经科学和AI帮你找工作，Pymetrics获得800万美元融资
有人靠心理测试找工作，有人用星座找公司，现在有人开始用神经科学帮你找工作了。
美国公司Pymetrics利用人工智能和神经科学小游戏来帮用户匹配最合适的工作。这家公司宣布完成了800万美元融资，Jazz Venture Partners领投，新进投资者Workday Ventures和原有投资者Khosla Ventures、 Randstad Innovation Fund和BBG Ventures参投。这笔资金将用于人才招募，公司目前有45名员工，分别在纽约、旧金山、伦敦和新加坡。
Pymetrics 成立于2013年，迄今为止已经融了超过1700万美元。
这家公司总部位于纽约，跟一般人用学历和院校来评价求职者不同，他们用认知和情感方面的元素来评价应聘者，具体方式是让他们玩一套神经科学小游戏。用户需要完成包括虚拟金钱交易、键盘点击等至少12个游戏，才能收到完整的职业评估。
接下来，Pymetric的人工智能系统会分析应聘者的结果，将其与公司中表现最好的员工比较。
Pymetrics 的联合创始人兼CEO Frida Polli对VentureBeat 解释：“我们会收集各行各业专业人士的密集行为数据，利用深度学习建模，分析究竟是哪些特质，让这些成功人士比一般人优秀。”
Pymetrics的服务对求职者免费开放，公司的盈利模式是向B端用户收费。公司会给企业客户提供定制化的算法模型，让他们通过平台挑选出有潜力的人才。该服务按年按服务级别收费。
根据Pymetrics给出的数据，全球目前有50个国家正在使用他们的平台，包括Unilever和埃森哲（Accenture）；在求职者这边，则一共有50万名用户。
According to Pymetrics, more than 50 companies around the world currently use the platform, including Unilever and Accenture. On the job seeker side, there are more than 500,000 users.
VentureBeat担心，有些算法模型会不会带有人类的偏见，或者某些政治不正确的因子。Polli则回应：“我们的算法不会收集任何人口统计学方面的数据，而且我们会选用统计工具剔除任何模型中的偏见。”
公司CEO将心理学加大数据的服务公司CEB-SHL 和 IBM Kenexa等传统平台视作竞品，实际上很多新型创业公司正在将AI技术用户职业匹配，比如Leap.ai、Teamable、Beamery和Mya Systems。
除了这800万投资之外，Pymetrics还从洛克菲勒基金会（ The Rockefeller Foundation）获得一笔补助，具体金额未透露。这笔资金将用户帮助未充分就业的年轻人，让他们学以致用。
本文翻译自 venturebeat.com，原文链接。如若转载请注明出处。
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
800万美元
800万美元
800万美元
800万美元
800万美元
800万美元
800万美元
800万美元
800万美元
800万美元
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