2020年8月31日

预计从数百每天阅读200000字以上,如果不是数以千计,文件,要求金融分析师执行不可能的。底漆使用AI相当于压缩技术应用于这座山的数据来帮助使工作更容易为他们以及分析师在一系列其他行业。五岁的公司,总部设在旧金山,建造了一个自然语言处理和机器学习平台,基本上所有分析师的阅读和整理的一小部分的时间通常带他们。任何一个给定的分析师可能监控,无论它是一个自然灾害,信用违约或地缘政治事件,底漆斜杠小时的人类几秒钟的分析研究。软件梳子通过大量的内容,突出了相关信息,如引号和事实,并组装成相关的列表。它提取大量主题必需品在几秒钟内。“我们训练模型来模拟人类行为,”巴里说的画匠,商业销售的副总裁底漆。“这真是一个强大的分析平台,使用自然语言处理和机器学习表面和总结信息。“1000位分析师的力量使用底漆的平台上运行的NVIDIA gpu类似于给分析师虚拟员工提供近乎即时的结果。该软件可以分析和报告从财务报告数以万计的文档,内部专利内容,社会媒体,30000 - 40000年新闻来源和其他地方。 “Every time an analyst wants to know something about Syria, we cluster together documents about Syria, in real time,” said Ethan Chan, engineering manager and staff machine learning engineer at Primer. “The goal is to reduce the amount of effort an analyst has to expend to process more information.” Primer has done just that to the relief of its customers, which includes financial services firms, government agencies and an array of Fortune 500 companies. As powerful as Primer’s natural language processing algorithms are, up until two years ago they required 20 minutes to deliver results because of the complexity of the document clustering they were asking CPUs to support. “The clustering was the bottleneck,” said Chan. “Because we have to compare every document with every other document, we’re looking at nearly a trillion flops for a million documents.” GPUs Slash Analysis Times Primer’s team added GPUs to the clustering process in 2018 after joining NVIDIA Inception — an accelerator program for AI startups — and quickly slashed those analysis times to mere seconds. Primer’s GPU work unfolds in the cloud, where it makes equally generous use of AWS, Google Cloud and Microsoft Azure. For prototyping and training of its NLP algorithms such as Named Entity Recognition and Headline Generation (on public, open-source news datasets), Primer uses instances with NVIDIA V100 Tensor Core GPUs . Model serving and clustering happens on instances with NVIDIA T4 GPUs , which can be dialed up and down based on clustering needs. The company also uses a wrapper called CuPy, which allows for CUDA-powered acceleration of GPUs on Python . But what Chan believes is Primer’s most innovative use of GPUs is in acceleration of its clustering algorithms. “Grouping documents together is not something anyone else is doing,” he said, adding that Primer’s success in this area further establishes that “you can use NVIDIA for new use cases and new markets.” Flexible Delivery Model With the cloud-based SaaS model, customers can increase or decrease their analysis speed, depending on how much they want to spend on GPUs. Primer’s offering can also be deployed in a customer’s data center. There, the models can be trained on a customer’s IP and clustering can be performed on premises. This is an important consideration for those working in highly regulated or sensitive markets. Analysts in finance and national security are currently Primer’s primary users, however, the company could help anyone tasked with combing through mounds of data actually make decisions instead of preparing to make decisions.