Distracted AI: Integrating Neuroscience-Inspired Attention and Distraction Learning in ANN

This paper introduces an advanced approach in artificial intelligence (AI) that incorporates neuroscience-inspired attention mechanisms and distractor learning into artificial neural networks (ANNs). This novel method significantly reduces model size and RAM consumption during training, offering a more efficient and resource-effective solution for AI development. By emulating the human brain’s ability to process information and manage distractions, our approach not only addresses common AI challenges such as model collapse and bias amplification but also contributes to a reduction in computational resource requirements. This is particularly beneficial in scenarios where computing power and memory are limited, making AI more accessible and sustainable. Furthermore, the smaller model footprint and lower resource demand pave the way for broader applications of AI in various fields, including those with restricted hardware capabilities. This paper details the theoretical framework of this approach, its practical implementation, and the potential implications for future AI developments, emphasizing the balance between advanced AI capabilities and resource efficiency.