Will Transfer Learning Make Future AI Models Faster to Build?

Key Concepts : Pretrained Model: A model trained on a large dataset (like ImageNet for images, or BERT for language). Fine-tuning: The pretrained model is adapted to a new task by continuing training on the new, typically smaller, dataset. Feature Extraction: Using the pretrained model’s learned representations (features) as input to a new model without retraining the whole network. Common Use Cases of Transfer Learning : Computer Vision: Using models like ResNet or VGG pretrained on ImageNet to classify medical images or detect defects. Natural Language Processing: Using BERT Will Transfer Learning Make Future AI Models Faster to Build? As artificial intelligence advances, a natural question arises: Will each new generation of AI models take less time to build, thanks to transfer learning? At first glance, it seems obvious, if we can reuse what we've already learned, shouldn't that make things faster? The truth is a bit more nuanced. While transfer learning is a game-changer, ...