IML 2016 - 2016 workshop on Interactive Machine Learning: Connecting Humans and Machines

In recent years there has been an increased interest in the design of algorithms that facilitate machine learning with the help of human interaction. Such approaches often referred to as Interactive Machine Learning (IML), are based on a coupling of human input and machines during the learning process. Specifically IML is concerned with answering questions related to how machines can interact with people to solve problems more efficiently than autonomous systems (which often require intense engineering effort to be effective learning systems).
With the exponential growth in computing power and a focus on enhancing user-experience through technology, there exist several opportunities where humans are required to interact with machines to solve problems. Canonical applications of IML include scenarios involving humans interacting with robots to teach them to perform certain tasks, humans helping virtual agents play computer games by giving them feedback on their performance or using a teaching curriculum to guide the machine learning. However there exist a number of challenges in this area of research ranging from the choice of human interaction modality to the design of algorithms suitable for interactive learning and appropriate representations for the problem. As such these challenges span a variety of scientific disciplines and application domains like artificial intelligence, machine learning, human-computer interaction, cognitive science and robotics. The goal of the workshop is to bring together researchers in these fields to discuss the design and analysis of algorithms that facilitate Interactive Machine Learning. It is an opportunity for scientists in these disciplines to come together, share their perspectives, discuss solutions to common problems and highlight the challenges in the field to help guide future research in IML.
The target audience for the workshop includes people who are interested in using machines to solve problems by having a human be an integral part of the learning process. This workshop serves as a platform where researchers can discuss approaches that bridge the gap between humans and machines and get the best of both worlds.