• Wataru Kawabe Graduate School of Information Science and Technology, The University of Tokyo

• Yusuke Sugano Associate Professor, Institute of Industrial Science, The University of Tokyo

Recent advances in machine learning (ML), especially deep neural networks, have greatly expanded the opportunities for various real-world applications in our daily lives. However, while ML has great potential, it is still difficult for novice users to design a model for their own purpose since building an ML model requires programming skills and mathematical knowledge. Interactive machine learning (IML) aims at providing a method for them to interact with ML algorithms and prototype their own models. There have been many research attempts to create interactive image/sound/text recognition systems, and this time I show you some examples I found worth checking. I also introduce my own work attempting to provide a generic image recognition model with text as output, and how novice users responded to the prototype system and solved various image recognition tasks with it.

Wataru Kawabe is a master’s student in the Graduate School of Information Science and Technology at the University of Tokyo, specializing in computer science. His research interests include applied machine learning and human-computer interaction, and his current research focuses on understanding end-users through interaction with image/sound/text recognition applications.