A Marketplace and Tool Suite for the quick creation and shared use of sophisticated Conversational Interface Language Classifiers.

人工智能对话界面(CI)是一种自动化技术，通过文本或语音界面为用户永久待命--通常通过Facebook Messenger等Instant Messenger频道和Alexa等语音频道访问。CI承诺在用户和企业之间实现全天候自动化、无摩擦的通信，以实现快速、方便的任务完成。对于希望从商业优势CI中获益的企业来说，存在着重大障碍。市场上的软件工具不足以开发商业实力的CI。收集CI培训数据也存在重大挑战。这些困难使得构建超出原型质量的配置项几乎是不可能的。该项目的专家联盟旨在使没有人工智能专业知识的企业开发团队快速且廉价地创建复杂的商业实力的配置项。提供的解决方案消除了进入市场的障碍，构成了改变游戏规则的模式，使各种规模的企业在国际上都有能力。该联盟在广泛业务范围内的机器学习和商业报文传送集成方面拥有深厚的专业知识，并致力于设计创新技术，以应对CI创建挑战。语言分类器市场的核心要素将在该项目中提供，使公司能够使用预先构建的、可即时配置的机器学习语言分类器模板来创建高质量的CI，而无需机器学习专业知识。

"An Artificially Intelligent Conversational Interface (CI) is automated technology permanently on call for users via text or voice interfaces - accessed typically via Instant Messenger channels such as Facebook Messenger and voice channels like Alexa. CIs promise 24/7 automated, frictionless communication between users and businesses to enable quick, convenient task completion.Significant barriers exist for businesses wishing to benefit from commercial strength CIs. Software tools on the market are inadequate for developing commercial-strength CIs. There are also significant challenges collecting CI training data. These difficulties render it practically impossible to build CIs beyond prototype quality.This project's expert consortium aims to make it quick and inexpensive for enterprise development teams without expertise in AI to create sophisticated commercial-strength CIs. The delivered solution removes barriers to market entry, constituting game-changing models that empower enterprises of all sizes internationally. The consortium has deep expertise in machine-learning and commercial messaging integration across a broad business spectrum, and is positioned to design innovative technology that addresses CI creation challenges.Core elements of a Language Classifier Marketplace will be delivered in this project, enabling companies to use prebuilt, instantly configurable machine-learning language classifier templates to create high-quality CIs without machine-learning expertise."