Neuro-Symbolic Artificial Intelligence: A Task-Directed Survey in the Black-Box Models Era

TL;DR

This survey reviews recent task-specific advancements in Neuro-Symbolic AI, highlighting how integrating symbolic systems with neural networks can improve explainability and reasoning, despite challenges in semantic generalization.

Contribution

It provides a comprehensive overview of task-oriented Neuro-Symbolic methods, emphasizing their potential for explainability and reasoning in real-world applications.

Findings

NeSy methods can enhance explainability in AI systems.

Recent advancements show promise in NLP and computer vision.

Challenges remain in semantic generalization and complex domain handling.

Abstract

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.