To gain a clearer understanding of the differences between Artificial Intelligence, Machine Learning, and Deep learning, let us analyze them in a tabular format.
| Category | Artificial Intelligence (AI) | Machine Learning (ML) | Deep Learning (DL) |
| Scope | Broad | Subset of AI | Subset of ML |
| Brief Definition | Generalized Intelligence | Learning from data | Neural network-based |
| Definition | AI mimics or simulates human intelligence to perform tasks or make decisions. | A subset of AI that uses algorithms to learn patterns from data and make predictions or decisions. | A subset of Machine Learning that employs neural networks to perform complex tasks. |
| Data Dependency | May or may not require large datasets; can use predefined rules. | Requires labeled datasets for training; can also use rules. | Highly dependent on large labeled datasets; limited use of predefined rules. |
| Approach | AI involves the simulation of human intelligence to solve complex problems. | ML relies on statistical techniques to enable machines to learn from the data (past). | DL utilizes neural networks with multiple layers to extract features and patterns from the data. |
| Training | Rule-based or knowledge-based systems | Supervised, unsupervised, or reinforcement learning | Supervised learning with extensive labeled data |
| Flexibility | High; Can handle a variety of tasks; more flexible. | Moderate; Flexible but focused on specific tasks. | High, especially in complex tasks like image and speech recognition. |
| Examples | Virtual assistants like Siri or Alexa can understand and respond to your voice.
Basic navigation systems. Filtering out spam emails. |
Spam filters that learn to recognize and filter out spam emails based on your actions.
Learning to drive, real-time decision-making. Pattern recognition, spam classification |
Image recognition systems that can identify objects in pictures, like recognizing a cat in a photo.
Advanced decision-making in self-driving cars. Deep analysis of email content |
