Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks. (arXiv:2201.05489v1 [cs.CV])

Representation is a core issue in artificial intelligence. Humans use
discrete language to communicate and learn from each other, while machines use
continuous features (like vector, matrix, or tensor in deep neural networks) to
represent cognitive patterns. Discrete symbols are low-dimensional, decoupled,
and have strong reasoning ability, while continuous features are
high-dimensional, coupled, and have incredible abstracting capabilities. In
recent years, deep learning has developed the idea of continuous representation
to the extreme, using millions of parameters to achieve high …

arxiv cv intelligence language machine networks neural networks