Conversing Learning: Active Learning and Active Social Interaction for Human Supervision in Never-Ending Learning Systems

Abstract

The Machine Learning community have been introduced to NELL (Never-Ending Language Learning), a system able to learn from web and to use its knowledge to keep learning infinitely. The idea of continuously learning from the web brings concerns about reliability and accuracy, mainly when the learning process uses its own knowledge to improve its learning capabilities. Considering that the knowledge base keeps growing forever, such a system requires self-supervision as well as self-reflection. The increased use of the Internet, that allowed NELL creation, also brought a new source of information on-line. The social media becomes more popular everyday and the AI community can now develop research to take advantage of these information, aiming to turn it into knowledge. This work is following this lead and proposes a new machine learning approach, called Conversing Learning, to use collective knowledge from web community users to provide self-supervision and self-reflection to intelligent machines, thus, they can improve their learning task. The Conversing Learning approach explores concepts from Active Learning and Question Answering to achieve the goal of showing what can be done towards autonomous Human Computer Interaction to automatically improve machine learning tasks.