With the rise of Artificial Intelligence, we are fortunate to witness the transition from achieving machine’s automation to achieving machine’s autonomy. On one hand, the success of Artificial Intelligence is guaranteed by the availability of big data which is the result of the formation of large systems that are interconnected by various networks. On the other hand, the importance of Artificial Intelligence is due to the urgent demand for self-intelligence by robots and machines of tomorrow. Interestingly, the critical step toward achieving machine’s self-intelligence is the ability of designing large knowledge models instead of improving existing databases. In this invited talk, I will share with the audience our research works which aim at providing a general guiding principle for the design of a large knowledge model under the new paradigm of AI 3.0. The published research findings could be found inside 1) Xie M., *Jayakumar K. S. and *Chia H. F., 2004, Meaning-centric Framework for Natural Text/Scene Understanding by Robots, International Journal of Humanoid Robotics, Vol. 1, No. 2, pp. 375-407, and 2) Xie M.,2024, Top-down Design of Human-like Teachable Mind, Special Issue in Celebrating IJHR’s 20th Year Anniversary, International Journal of Humanoid Robotics.
Jayakumar K. S. and Xie M. (2010). Natural Language Understanding by Robots, LAP LAMBERT Academic Publishing Co.
Xie M., Chen H. and Hu Z. C. (2021). New Foundation of Artificial Intelligence, World Scientific Publishing Co.