Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era
This work addresses the problem of limited semantic generalizability and explainability in AI systems for researchers and practitioners in the field of Artificial Intelligence.
This survey explores the integration of symbolic computing with neural networks, known as Neuro-Symbolic methods, to enhance explainability and reasoning capabilities in AI systems, particularly in Natural Language Processing and Computer Vision fields. The survey aims to provide a resource for researchers exploring explainable Neuro-Symbolic methodologies for real-life tasks and applications.
The integration of symbolic computing with neural networks has intrigued researchers since the first theorizations of Artificial intelligence (AI). The ability of Neuro-Symbolic (NeSy) methods to infer or exploit behavioral schema has been widely considered as one of the possible proxies for human-level intelligence. However, the limited semantic generalizability and the challenges in declining complex domains with pre-defined patterns and rules hinder their practical implementation in real-world scenarios. The unprecedented results achieved by connectionist systems since the last AI breakthrough in 2017 have raised questions about the competitiveness of NeSy solutions, with particular emphasis on the Natural Language Processing and Computer Vision fields. This survey examines task-specific advancements in the NeSy domain to explore how incorporating symbolic systems can enhance explainability and reasoning capabilities. Our findings are meant to serve as a resource for researchers exploring explainable NeSy methodologies for real-life tasks and applications. Reproducibility details and in-depth comments on each surveyed research work are made available at https://github.com/disi-unibo-nlp/task-oriented-neuro-symbolic.git.