What is Learning? A primary discussion about information and Representation

by   Hao Wu, et al.

Nowadays, represented by Deep Learning techniques, the field of machine learning is experiencing unprecedented prosperity and its influence is demonstrated in academia, industry and civil society. "Intelligent" has become a label which could not be neglected for most applications; celebrities and scientists also warned that the development of full artificial intelligence may spell the end of the human race. It seems that the answer to building a computer system that could automatically improve with experience is right on the next corner. While for AI and machine learning researchers, it is a consensus that we are not anywhere near the core technique which could bring the Terminator, Number 5 or R2D2 into real life, and there is not even a formal definition about what is intelligence, or one of its basic properties: Learning. Therefore, even though researchers know these concerns are not necessary currently, there is no generalized explanation about why these concerns are not necessary, and what properties people should take into account that would make these concerns to be necessary. In this paper, starts from analysing the relation between information and its representation, a necessary condition for a model to be a learning model is proposed. This condition and related future works could be used to verify whether a system is able to learn or not, and enrich our understanding of learning: one important property of Intelligence.


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