Future trends in neural network research include enhancing model efficiency through techniques like sparsity and quantization. These advancements aim to reduce resource consumption and make models more accessible.
Multi-modal models, which process diverse data types (e.g., text and images), are gaining traction. OpenAI’s CLIP and Google’s PaLM-E exemplify this trend.
Other areas include neural architecture search (NAS), explainable AI, and advancements in unsupervised and semi-supervised learning. As neural networks evolve, their applications in fields like robotics and personalized medicine are expected to expand.