AI can be divided into machine learning and deep learning. Deep learning, a type of machine learning, uses artificial neural networks to mimic the human brain's learning process. Deep learning is effective with large amounts of data, while traditional machine learning works better with smaller datasets. Deep learning requires high-quality infrastructure for training due to its numerous parameters. While deep learning is fast during testing, machine learning methods like k-nearest neighbors may slow down with more data. Deep learning can provide high performance in tasks like document relevance scoring, but the reasoning behind the results may be unclear. Data curation is crucial for tasks like image processing, involving labeling, annotation, and segmentation.