What is Learning? A primary discussion about information and Representation
Hao Wu

TL;DR
This paper explores the fundamental concept of learning in AI by analyzing the relationship between information and its representation, proposing a necessary condition for models to be considered capable of learning.
Contribution
It introduces a formal necessary condition for learning models based on information and representation, aiding in assessing a system’s ability to learn.
Findings
Proposes a necessary condition linking information and representation for learning
Provides a framework to verify if a system can learn
Enhances understanding of learning as a property of intelligence
Abstract
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…
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Taxonomy
TopicsEducation and Critical Thinking Development
