The rapid deployment of low-cost ubiquitous sensing devices - including RFID tags and readers, global positioning systems, wireless audio, video, and bio sensors - makes it possible to create instrumented environments and to capture the physical and communicative interaction of an individual with these environments in a digital register. One of the grand challenges of current AI research is to process this multimodal and massive data stream, to recognize, classify, and represent its digital content in a context-sensitive way, and finally to integrate behavior understanding with reasoning and learning about the individual's day by day experiences. This augmented personal memory is always accessible to its owner through an Internet-enabled smartphone using high-speed wireless communication technologies. In this contribution, we discuss how such an augmented personal memory can be built and applied for providing the user with context-related reminders and recommendations in a shopping scenario. With the ultimate goal of supporting communication between individuals and learning from the experiences of others, we apply this novel methods as the basis for a specific way of exploiting memories - the sharing of augmented personal memories in a way that doesn't conflict with privacy constraints. © 2006 Springer-Verlag Berlin/Heidelberg.
Wahlster, W., Kröner, A., & Heckmann, D. (2006). SharedLife: Towards selective sharing of augmented personal memories. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4155 LNAI, pp. 327–342). https://doi.org/10.1007/11829263_18