Multimodal processing enables AI systems to handle multiple input types simultaneously, like text, images, audio, and video, rather than processing each modality separately. It's becoming essential as devs build more sophisticated applications that need to understand rich, mixed-media content naturally.

Pruning removes unnecessary connections or neurons from trained neural networks to reduce model size whilst maintaining accuracy. It identifies which weights contribute least to the final output and eliminates them, creating sparse networks that run faster and use less memory. This technique is crucial for deploying models on edge devices where computational resources are limited.