What is AI / ML?
AI is the broader concept encompassing the development of intelligent computer systems, while ML is a specific approach within AI that involves learning from data to improve performance on tasks.
Breakdown
Artificial Intelligence (AI) and Machine Learning (ML) are related concepts, but they are not interchangeable terms. Here's the difference between the two:
| Artificial Intelligence (AI) |
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| Artificial Intelligence is a broad field of computer science that aims to create machines or systems that can perform tasks that would typically require human intelligence. |
| AI encompasses a wide range of techniques, methodologies, and approaches to simulate human intelligence, including problem-solving, decision-making, natural language processing, perception, and learning. |
| AI can be further categorized into narrow AI (also known as weak AI), which is designed to perform specific tasks within a limited domain, and general AI (also known as strong AI or AGI), which would exhibit human-like intelligence across a wide range of tasks and domains. |
| Machine Learning (ML) |
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| Machine Learning is a subset of AI that focuses on the development of algorithms and statistical models that enable computers to learn and improve their performance on a specific task without being explicitly programmed. |
| ML algorithms learn from data, identifying patterns, trends, and relationships within the data to make predictions or decisions. |
| ML techniques include supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning (a subfield of ML that involves neural networks with many layers). |
| ML is widely used in various applications, such as image recognition, natural language processing, recommendation systems, autonomous vehicles, and medical diagnosis. |
Summary
AI is the broader concept that encompasses any technique or system that mimics human intelligence, while Machine Learning is a specific approach within AI that focuses on enabling computers to learn from data and improve their performance on specific tasks without explicit programming. ML is a subset of AI, but AI includes other approaches beyond ML, such as rule-based systems, expert systems, and symbolic AI.