Breaking the Mould: Rethinking Deep Learning with Unconventional Architectures

Abstract

Deep learning has become the cornerstone of modern artificial intelligence, enabling breakthroughs in areas such as computer vision, natural language processing, and robotics. However, traditional deep-learning approaches face significant challenges, including data hunger, computational costs, and a lack of interpretability. This chapter explores unconventional pathways in deep learning that address these limitations and push the boundaries of AI. This paper delves into neuroevolution, spiking neural networks, capsule networks, and quantum machine learning, highlighting their unique advantages, challenges, and applications. Additionally, the study discussed the ethical considerations of these emerging technologies, emphasising the need for responsible development. By examining these unconventional approaches, this chapter aims to inspire researchers and practitioners to explore new frontiers in deep learning and unlock its full potential.