Xie Ming, Ph.D

Associate Professor,

Nanyang Technological University (Singapore)

KnowNet: A Large Knowledge Model

With the rise of Artificial Intelligence, we are fortunate to witness the transition from achieving machinefs automation to achieving machinefs 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fs self-intelligence is the ability of designing large knowledge models instead of improving existing databases. In this keynote speech, 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 Artificial Intelligence.

 

References

  1. Xie M., Chen H. and Hu Z. C., (2021), New Foundation of Artificial Intelligence, World Scientific Publishing Co., 404pp.
  2. *Jayakumar K. S. and Xie M., (2010), Natural Language Understanding by Robots, LAP LAMBERT Academic Publishing Co., 128pp.
  3. Xie M., (2003), Fundamentals of Robotics: Linking Perception to Action, World Scientific Publishing Co., 716pp.
  4. Xie M, Wang X. H. and Li J. H., 2025, A Hybrid Strategy for Achieving Robust Matching Inside the Binocular Vision of a Humanoid Robot, Open Access Journal of Mathematics.
  5. Xie M., **Fang Yuhui and **Lai Tingfeng, 2025, New Solution to 3D Projection in Human-like Binocular Vision, International Journal of Humanoid Robotics.
  6. Xie M., 2024, Top-down Design of Human-like Teachable Mind, Special Issue in Celebrating IJHRfs 20th Year Anniversary, International Journal of Humanoid Robotics.
  7. Xie M. **Lai Tingfeng  and **Fang Yuhui, 2023, A New Principle Toward Robust Matching in Human-like Stereovision, Open Access Journal of Biomimetics.
  8. Xie M. and Wang X. H., 2025, Bottom-Up Approach to Knowledge Extraction from Digital Images, International Conference on Artificial Intelligence for Sustainable Society, 29-30 November, Kobe, Japan.
  9. *Tanumara R. C., Xie M. and Au C. K., 2006, Learning Human-like Color Categorization through Interaction, International Journal of Computational Intelligence, Vol. 3, No. 4, pp. 338-345.
  10. 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.