Interactive Machine Learning
Interactive Machine Learning (IML) is the design and implementation of algorithms and intelligent user interface (IUI) frameworks that facilitate machine learning (ML) with the help of human interaction.
With the convergence of Artificial intelligence and Machine Learning, IML is where the Human-Computer Interaction (HCI) and IUI community meets the ML community. We also combine contributions from related fields such as data science, cognitive science, computer graphics, design or the arts, and natural language processing, data mining/data analytics, and knowledge representation and reasoning. Our focus is to improve the interaction between humans and machines to update ML models, by leveraging both state-of-the-art HCI and IUI approaches, as well as solutions that involve state-of-the-art ML techniques.
We can “assist” AI systems in becoming self-sustaining, “lifelong” learners. This includes to (1) develop insights into the importance of the social and cultural contexts of machine learning; (2) create ML systems that actively seek information; (3) realise the need to pay attention to the incomplete context understandings and naive generalisations that ML systems, in particular end-to-end systems, bring with them to a given subject. We focus on multimodal multisensor interfaces for ML.