Symbolic Representation

Symbolic representation in artificial intelligence focuses on encoding information using discrete symbols, aiming to bridge the gap between data-driven learning and human-like reasoning. Current research emphasizes integrating symbolic methods with neural networks, exploring architectures like transformers and employing techniques such as knowledge distillation, Bayesian filtering, and self-supervised learning to create more robust and interpretable models. This work is significant because it addresses limitations in current deep learning approaches, such as a lack of explainability and poor generalization, paving the way for more reliable and trustworthy AI systems across diverse applications.