Answer :
To understand the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL), it's helpful to think about them as a set of nested technologies.
Artificial Intelligence (AI) is the broadest concept. It encompasses any computer program that exhibits behavior typically requiring human intelligence. This includes reasoning, problem-solving, understanding language, and perceiving the environment. AI is like the overarching umbrella that aims to create systems capable of performing tasks that require human-like intelligence.
Machine Learning (ML) is a subset of AI. ML focuses on creating algorithms that allow computers to learn from and make predictions based on data. Instead of being explicitly programmed to perform a task, ML systems use statistical techniques to improve their performance over time as they are exposed to more data. This makes ML an essential component of modern AI applications.
Deep Learning (DL) is a further subset of ML. DL involves neural networks with many layers (hence 'deep') that simulate the way humans gain certain types of knowledge. It has become particularly useful for tasks like image and speech recognition due to its ability to handle large amounts of structured and unstructured data.
Based on this explanation, the correct statement is:
- ML is a subset of AI, and DL is a subset of ML.
This hierarchical relationship makes it clear how these technologies build upon each other, with AI being the most general, and DL being the most specific. Each level incorporates the capabilities of the previous, with deep learning extending the learning capabilities provided by machine learning, which in turn operates within the framework of artificial intelligence.
