Lifelong Personal Context Recognition

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.

Streaming and Learning the Personal Context

The representation of the personal context is complex and essential to improve the help machines can give to humans for making sense of the world, and the help humans can give to machines to improve their efficiency. We aim to design a novel model representation of the personal context and design a learning process for better integration with machine learning. We aim to implement these elements into a modern system architecture focus in real-life environments. Also, we show how our proposal can improve in specifically related work papers. Finally, we are moving forward with a better personal context representation with an improved model, the implementation of the learning process, and the architectural design of these components.