SBIR Phase I: Automating Conversational Systems For Enterprise Customer Support

这个小企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力将来自于一个可扩展的、企业就绪的会话人工智能平台的开发，该平台使企业能够自动化客户支持请求，优化呼叫路由工作，使客户找到合适的人工支持代理，生成客户自助服务资源，如智能常见问题，增强人工员工的机器人功能。并启用个性化推荐，以支持客户的销售和购买决策。这种技术对任何需要与客户、员工和公众进行沟通的企业组织(B2C（企业对消费者）和B2B（企业对企业）都是有益的。随着客户服务和通信成本的上升，自动化对企业保持盈利能力和竞争优势至关重要。在社会层面上，对话界面使全世界的信息和资源访问民主化。世界上很大一部分人口生活在互联网接入能力差、设备拥有率低的地区。许多人还遭受身体上的限制，如视力受损或运动控制受限。通过短信和语音等对话界面加速技术获取已被证明可以通过远程医疗、个人理财和教育等应用改变弱势群体的生活质量。这个小企业创新研究（SBIR）第一阶段项目旨在使机器具有人类水平的对话能力。教计算机像人类一样交流是实现人类水平机器智能（HLMI）的关键一步，也被称为人工通用智能（AGI）。当前最先进的自动对话系统方法使用了监督学习和深度神经网络架构实现的强化学习的结合。这些方法在学术研究中显示出解决棘手问题的希望，但在企业环境中表现好坏参半。这个第一阶段的项目扩展了当前的深度学习方法，开发了一种可重复的方法，用于注释和准备用于对话系统的企业数据，以及一种可扩展的、基于神经网络的体系结构，该体系结构可以通过最少的人工工程来引用外部知识来源。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project will result from the development of a scalable, enterprise-ready conversational AI platform that enables businesses to automate customer support requests, optimize call routing efforts to get customers to the right human support agent, generate customer self-service resources like smart FAQs, augment human staff with bot capabilities, and enable personalized recommendations to support sales and purchasing decisions for customers. Such technology is beneficial to any enterprise organization, B2C (business-to-consumer) and B2B (business-to-business), who needs to communicate to customers, employees, and the public. With rising customer service and communication costs, automation is critical for businesses to maintain profitability and competitive advantages. On a societal level, conversational interfaces democratize access to information and resources worldwide. A significant portion of the world's population lives in regions where internet access is poor and device ownership is rare. Many also suffer from physical limitations, such as impaired vision or limited motor control. Accelerating technology access through conversational interfaces such as SMS and voice has already been shown to transform quality of life for disadvantaged groups through applications in telemedicine, personal finance, and education.This Small Business Innovation Research (SBIR) Phase I project aims to enable human-level conversational ability in machines. Teaching computers to communicate like humans is a critical step to achieving human-level machine intelligence (HLMI), also called artificial general intelligence (AGI). Current state-of-the-art approaches to automated dialog systems use a combination of supervised learning and reinforcement learning implemented with deep neural network architectures. These approaches have shown promise on toy problems in academic research, but have mixed performance in enterprise settings. This Phase 1 project extends current deep learning approaches by developing a repeatable methodology for annotating and preparing enterprise data for use in dialog systems and a scalable, grounded neural network architecture that can reference external knowledge sources with minimal manual engineering.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.