Brain-Inspired AI with Hyperbolic Geometry
This perspective paper proposes a brain-inspired approach to improve machine learning models, but it is incremental as it builds on existing hyperbolic geometry research without presenting new experimental results.
The paper argues that incorporating hyperbolic geometry into artificial neural networks, inspired by the brain's hierarchical structure, can enhance accuracy, representation, and efficiency across tasks like natural language processing and computer vision, with empirical evidence showing it outperforms Euclidean models using fewer parameters and better generalization.
Artificial neural networks (ANNs) were inspired by the architecture and functions of the human brain and have revolutionised the field of artificial intelligence (AI). Inspired by studies on the latent geometry of the brain, in this perspective paper we posit that an increase in the research and application of hyperbolic geometry in ANNs and machine learning will lead to increased accuracy, improved feature space representations and more efficient models across a range of tasks. We examine the structure and functions of the human brain, emphasising the correspondence between its scale-free hierarchical organization and hyperbolic geometry, and reflecting on the central role hyperbolic geometry plays in facilitating human intelligence. Empirical evidence indicates that hyperbolic neural networks outperform Euclidean models for tasks including natural language processing, computer vision and complex network analysis, requiring fewer parameters and exhibiting better generalisation. Despite its nascent adoption, hyperbolic geometry holds promise for improving machine learning models through brain-inspired geometric representations.