Personalized Machine Learning: Towards Human-centered Machine Intelligence

Опубликовано 22 июля 2019, 17:31

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 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 these challenges. I will then show how this framework can be used to devise Personalized Deep Neural Networks for a challenging problem of the 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.

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