论文标题
部分可观测时空混沌系统的无模型预测
Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture
论文作者
论文摘要
随着人工智能（AI）成为现代生活的重要组成部分，人工智能素养对所有公民而言变得重要，而不仅仅是技术职业的公民。 AI教育材料的先前研究主要集中在引入术语以及AI用例和道德规范上，但是很少有人能够通过创建自己的机器学习模型来学习。因此，有必要为具有更适合适应性和灵活的平台提供充实的AI教育工具，以提供具有任何水平的技术经验的感兴趣的教育者，可以在其教材中使用。因此，我们为学生和老师提供开源工具（Build-A-Bot）的开发，不仅可以根据自己的课程材料创建自己的基于变压器的聊天机器人，而且还通过模型创建过程来学习AI的基础知识。本文的主要关注点是为学生创建一个界面，以通过使用自然语言管道来训练定制模型来根据自己的学校课程来回答问题，从而为学生创建一个界面。该模型使用其讲师给出的上下文（例如教科书的章节）来回答问题并部署在交互式聊天机器人/语音代理上。该管道教会学生数据收集，数据扩展，意图识别和问题的回答，通过使他们在这些过程中进行工作，同时创建其AI代理，与以前的聊天机器人作品不同，在这些过程中，学生和老师将这些机器人用作Black-boss，没有可定制的能力或机器人缺乏AI功能，并且具有对话脚本的大部分对话脚本，则基于规则。此外，我们的工具旨在使该管道的每个步骤都为中学级别的学生提供直觉。进一步的工作主要在于为学校提供我们的工具，并寻求学生和教师评估。
As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citizens, not just those in technology careers. Previous research in AI education materials has largely focused on the introduction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-a-Bot) for students and teachers to not only create their own transformer-based chatbots based on their own course material, but also learn the fundamentals of AI through the model creation process. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the majority of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for students at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations.
