We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the personal situational context that the human is in. We outline the key challenges that this task brings forth, namely (i) handling the human-like and ego-centric nature of the the user's context, necessary for understanding and providing useful suggestions, (ii) performing lifelong context recognition using machine learning in a way that is robust to change, and (iii) maintaining alignment between the AI's and human's representations of the world through continual bidirectional interaction. In this short paper, we summarize our recent attempts at tackling these challenges, discuss the lessons learned, and highlight directions of future research. The main take-away message is that pursuing this project requires research which lies at the intersection of knowledge representation and machine learning. Neither technology can achieve this goal without the other.

Lifelong Personal Context Recognition / Bontempelli, Andrea; Rodas Britez, Marcelo Dario; Li, Xiaoyue; Zhao, Haonan; Erculiani, Luca; Teso, Stefano; Passerini, Andrea; Giunchiglia, Fausto. - (2022). ((Intervento presentato al convegno Workshop on Human-Centered Design of Symbiotic Hybrid Intelligence tenutosi a Amsterdam, Netherlands nel 14th June 2022 [10.48550/arXiv.2205.10123].

Lifelong Personal Context Recognition

Andrea Bontempelli;Marcelo Rodas Britez;Xiaoyue Li;Haonan Zhao;Luca Erculiani;Stefano Teso;Andrea Passerini;Fausto Giunchiglia
2022

Abstract

We focus on the development of AIs which live in lifelong symbiosis with a human. The key prerequisite for this task is that the AI understands - at any moment in time - the personal situational context that the human is in. We outline the key challenges that this task brings forth, namely (i) handling the human-like and ego-centric nature of the the user's context, necessary for understanding and providing useful suggestions, (ii) performing lifelong context recognition using machine learning in a way that is robust to change, and (iii) maintaining alignment between the AI's and human's representations of the world through continual bidirectional interaction. In this short paper, we summarize our recent attempts at tackling these challenges, discuss the lessons learned, and highlight directions of future research. The main take-away message is that pursuing this project requires research which lies at the intersection of knowledge representation and machine learning. Neither technology can achieve this goal without the other.
Arxiv, Computer Science, Artificial Intelligence
New York
arXiv preprint
Bontempelli, Andrea; Rodas Britez, Marcelo Dario; Li, Xiaoyue; Zhao, Haonan; Erculiani, Luca; Teso, Stefano; Passerini, Andrea; Giunchiglia, Fausto
Lifelong Personal Context Recognition / Bontempelli, Andrea; Rodas Britez, Marcelo Dario; Li, Xiaoyue; Zhao, Haonan; Erculiani, Luca; Teso, Stefano; Passerini, Andrea; Giunchiglia, Fausto. - (2022). ((Intervento presentato al convegno Workshop on Human-Centered Design of Symbiotic Hybrid Intelligence tenutosi a Amsterdam, Netherlands nel 14th June 2022 [10.48550/arXiv.2205.10123].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/348182
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