Interaction Is the Key to Machine Learning Applications

The traditional field of Machine Learning is concerned with techniques for modifying the behavior of a computer agent over time in order to improve its usefulness to people. This problem has traditionally been formulated as an abstract mathematical problem of inducing a generalized function from assertions representing the "perceptions" or "experience" of the agent. This paper argues that this formulation of the Machine Learning problem is unnecessarily restrictive, and leaves out consideration of interaction between the human teacher and the computer learner during the learning process. Issues such as graphical representation of the input examples and visible feedback about the results of the learning process cannot be abstracted out because they are critical determinants of what learning problems the user will asked out because they are critical determinants of what learning problems the user will ask the system to solve, and whether the user will be satisfied with the system's behavior. Programming by Example [or Programming by Demonstration] systems try to integrate modern graphical user interfaces with Machine Learning techniques, and thus run up against these issues. While traditional learning techniques are useful for Programming by Example, they must be evaluated in terms of how they affect user interaction rather than just by such abstract measures as "speed" of learning or expressiveness of learned descriptions.