| Definition | A subset of AI that focuses on algorithms that allow computers to learn from and make predictions or decisions based on data without explicit programming. | A broader field of computer science that aims to create intelligent machines capable of simulating human-like intelligence, including problem-solving, reasoning, learning, and decision-making. |
| Objective | To enable computers to learn from data and improve performance on specific tasks without human intervention. | To develop systems that can perform tasks typically requiring human intelligence, such as understanding natural language, recognizing objects, and making decisions. |
| Scope | Limited to specific tasks or domains where it is trained. | Encompasses a wide range of applications, including but not limited to machine learning. |
| Examples | – Predicting stock prices based on historical data. – Identifying spam emails in your inbox. – Recognizing handwritten digits. | – Natural language processing (e.g., chatbots, language translation). – Computer vision (e.g., image recognition, object detection). – Robotics and autonomous systems. |
| Dependency on Data | Highly dependent on labeled or unlabeled training data. | Relies on data, but not all AI systems require extensive training data like in machine learning. Some AI systems use rule-based approaches or symbolic reasoning. |
| Human Intervention | Requires human experts to curate and label data for training. | Can function with or without human intervention, depending on the level of sophistication and design. Some AI systems can adapt and learn from new data without constant human guidance. |
| Adaptability | Can adapt and improve performance with new data. | AI systems can be designed to adapt and learn from new information, making them more flexible and capable of handling changing environments. |
| Types | Supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, etc. | AI includes machine learning techniques but also involves other approaches like expert systems, genetic algorithms, and knowledge representation and reasoning methods. |
| Relation to AI | A subset of AI that focuses on specific techniques and algorithms for data-driven learning. | An overarching field that encompasses machine learning as one of its many subfields. AI covers a broader range of methods and approaches beyond just data-driven learning. |
