머신 인텔리전스 연구실

Question answering system has long been considered a primary objective of artificial intelligence. The advancement of the QA system has attracted huge interests from the academic and industry community these days. We have been working on developing ranking algorithms that can select the best answer among the candidates. We also investigate the human-like QA system that generates answers in the natural language form in aligning with the given context. Furthermore, our research aims to fully incorporate an external knowledge to the QA system for integrating deep neural network-based model and human-generated expertise knowledge-base. This is a joint work with Adobe Research . There has been growing interest in human-like dialogue system such as Apple Siri, Google Assistant and other intelligence assistant services. However, current chat-bots tend to be incapable of recognizing context of utterances, often causing irritation and inconvenience. To overcome these difficulties, we are developing a Deep Learning-based dialogue model that can understand longitudinal context from dialogues. Furthermore, we aim to develop an intelligent personal assistant through combining the dialog system with speech recognition and image processing. This is a joint work with Social Computing Lab at KAIST. With the increasing use of AI in various industries such as medicine, law, and advertising, it is important to address the issue of fairness to eliminate algorithmic biases in automated decision-making processes. This means that AI’s results should not be influenced by any social prejudices present in the training data. Furthermore, AI models must protect personal information and avoid producing illegal results. We are working towards defining fairness in deep learning models and finding ways