Personalized Machine Learning for Human-centered Machine Intelligence

Abstract

Recent developments in AI and Machine Learning (ML) are revolutionizing traditional technologies for health and education by enabling more intelligent therapeutic and learning tools that can automatically perceive and predict user's behavior (e.g. from videos) or health status from user's past clinical data. To date, most of these tools still rely on traditional 'on-size-fits-all' ML paradigm, rendering generic learning algorithms that, in most cases, are suboptimal on the individual level, mainly because of the large heterogeneity of the target population. Furthermore, such approach may provide misleading outcomes as it fails to account for context in which target behaviors/clinical data are being analyzed. This calls for new human-centered machine intelligence enabled by ML algorithms that are tailored to each individual and context under the study. In this talk, I will present the key ideas and applications of Personalized Machine Learning (PML) framework specifically designed to tackle those challenges. The applications range from personalized forecasting of Alzheimer's related cognitive decline, using Gaussian Process models, to Personalized Deep Neural Networks, designed for classification of facial affect of typical individuals using the notion of meta-learning and reinforcement learning. I will then describe in more detail how this framework can be used to tackle a challenging problem of robot perception of affect and engagement in autism therapy. Lastly, I will discuss the future research on PML and human-centered ML design, outlining challenges and opportunities.