Experience, Imitation and Reflection; Confucius' Conjecture and Machine Learning
It addresses the problem of improving machine learning capabilities to be more human-like, but it is incremental as it builds on existing philosophical ideas without new empirical findings.
The paper explores how machines can learn by examining Confucius' three ways of learning—experience, imitation, and reflection—and discusses their implementation in artificial intelligence, though it does not present specific results or numbers.
Artificial intelligence recently had a great advancements caused by the emergence of new processing power and machine learning methods. Having said that, the learning capability of artificial intelligence is still at its infancy comparing to the learning capability of human and many animals. Many of the current artificial intelligence applications can only operate in a very orchestrated, specific environments with an extensive training set that exactly describes the conditions that will occur during execution time. Having that in mind, and considering the several existing machine learning methods this question rises that 'What are some of the best ways for a machine to learn?' Regarding the learning methods of human, Confucius' point of view is that they are by experience, imitation and reflection. This paper tries to explore and discuss regarding these three ways of learning and their implementations in machines by having a look at how they happen in minds.