"Ge Shu Zhi Zhi": Towards Deep Understanding about Worlds
December 19, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Baogang Hu, Weiming Dong
arXiv ID
1901.01834
Category
cs.AI: Artificial Intelligence
Citations
0
Venue
arXiv.org
Last Checked
4 months ago
Abstract
"Ge She Zhi Zhi" is a novel saying in Chinese, stated as "To investigate things from the underlying principle(s) and to acquire knowledge in the form of mathematical representations". The saying is adopted and modified based on the ideas from the Eastern and Western philosophers. This position paper discusses the saying in the background of artificial intelligence (AI). Some related subjects, such as the ultimate goals of AI and two levels of knowledge representations, are discussed from the perspective of machine learning. A case study on objective evaluations over multi attributes, a typical problem in the filed of social computing, is given to support the saying for wide applications. A methodology of meta rules is proposed for examining the objectiveness of the evaluations. The possible problems of the saying are also presented.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Artificial Intelligence
π
π
The Cartographer
R.I.P.
π»
Ghosted
Explanation in Artificial Intelligence: Insights from the Social Sciences
R.I.P.
π»
Ghosted
Federated Machine Learning: Concept and Applications
R.I.P.
π»
Ghosted
Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPR
R.I.P.
π»
Ghosted
DeepAR: Probabilistic Forecasting with Autoregressive Recurrent Networks
R.I.P.
π»
Ghosted
Rainbow: Combining Improvements in Deep Reinforcement Learning
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted