I-Corps: Commercializing the Integration of Human and Artificial Intelligence for Large Scale Multimedia Analysis

I-Corps：将人类和人工智能集成商业化以进行大规模多媒体分析

• 批准号: 1339552
• 负责人: Gerald Friedland
• 金额: $ 5万
• 依托单位: International Computer Science Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1339552&HistoricalAwards=false
• 关键词: Corps; Commercializing; Integration; Human; Artificial

Currently, crowdsourcing platforms are only being used to perform tasks that are easy for humans. Researchers have developed methods for using these systems to do tasks that are difficult for humans. They have developed methods to accomplish difficult tasks and with these techniques can produce solutions that are more accurate and efficient than any currently present in the marketplace. This method is a hybrid approach that combines artificial intelligence systems with low-cost crowdsourced labor enabling a flexible approach to the end user that enables them to analyze a wide variety of events with high accuracy while still achieving the time and cost savings associated with artificial intelligence-based systems.Multimedia content is a major part of people's ever day lives, and the ability to understand the data that is being acquired at a rapid pace will have a huge impact on society. Researchers aim to aid this by developing new techniques for the processing of large scale multimedia databases, creating methods for using existing crowdsourcing tools in a significantly more efficient and accurate way to produce high quality results, based on a n hybrid system of machine learning and human intelligence. This hybrid system could have an great public impact, leveraging the advantages of machine learning and human annotation simultaneously (having the machine learn from human crowdsourcers) providing a high accuracy solution at a lower cost than is currently possible.

目前，众包平台仅用于执行对人类来说容易的任务。研究人员已经开发出使用这些系统来完成人类难以完成的任务的方法。他们开发了完成困难任务的方法，并利用这些技术可以产生比目前市场上任何解决方案更准确、更高效的解决方案。这种方法是一种混合方法，将人工智能系统与低成本众包劳动力相结合，为最终用户提供灵活的方法，使他们能够高精度地分析各种事件，同时仍然节省与人工智能相关的时间和成本基于系统。多媒体内容是人们日常生活的重要组成部分，理解快速获取的数据的能力将对社会产生巨大影响。研究人员的目标是通过开发处理大规模多媒体数据库的新技术，创建基于机器学习和人类智能的混合系统，以更加高效和准确的方式使用现有众包工具来产生高质量结果的方法来帮助实现这一目标。这种混合系统可以产生巨大的公共影响，同时利用机器学习和人工注释的优势（让机器向人类众包学习），以比目前更低的成本提供高精度的解决方案。