This approach seeks to leverage the strengths of neural networks in pattern recognition and data-driven learning with the capabilities of symbolic AI, which deals with abstract, logical reasoning and knowledge representation. The significance of neurosymbolic AI lies in its potential to provide more interpretable, robust, and generalizable AI systems compared to purely data-driven approaches. By combining the interpretability and structured reasoning of symbolic AI with the learning efficiency of neural networks, neurosymbolic AI aims to enable machines to perform complex reasoning tasks, understand natural language at a deeper level, and interact with the world in a more human-like manner. This integration facilitates the development of AI systems capable of understanding context, making inferences, and applying knowledge in novel situations, thereby addressing some of the limitations of current AI technologies.
The concept of integrating symbolic and sub-symbolic AI has roots going back to the early days of AI research, but the term "neurosymbolic AI" and its current wave of research gained prominence in the late 2010s. This resurgence is attributed to advances in both machine learning and computational power, allowing for more sophisticated integration of neural and symbolic approaches.
Key contributors to the development of neurosymbolic AI include researchers from both the machine learning and symbolic AI communities. Efforts to develop neurosymbolic systems have been seen across various institutions, both academic and industrial, with IBM Research being notably active in this area, pushing forward the understanding and application of these integrated systems.