AI vs ML: Understanding the Difference

Artificial Intelligence (AI) and Machine Learning (ML)

• AI is a broader concept that enables machines or systems to sense, reason, act, or adapt like humans.

Artificial Intelligence (AI)

• AI aims to simulate human intelligence to solve problems.
• The goal is to develop an intelligent system that can perform complex tasks like humans.
• AI has a wide scope of applications.
• AI uses technologies to mimic human decision-making.
• AI works with all types of data: structured, semi-structured, and unstructured.
• AI systems use logic and decision trees to learn, reason, and self-correct.

Machine Learning (ML)

• ML is an application of AI that allows machines to extract knowledge from data and learn from it autonomously.
• ML aims to teach a machine to perform a specific task and provide accurate results by identifying patterns.
• The goal is to build machines that can learn from data to increase the accuracy of the output.
• ML has a limited scope of applications.
• ML uses self-learning algorithms to produce predictive models.
• ML can only use structured and semi-structured data.
• ML systems rely on statistical models to learn and can self-correct when provided with new data.

Key Differences between AI and ML

• AI encompasses a wide variety of approaches and algorithms, while ML is a specific approach.
• AI focuses on developing intelligent systems that can solve complex tasks, whereas ML focuses on teaching machines to perform specific tasks.
• AI has a broader scope of applications, whereas ML has a limited scope.

Learn about the relationship between Artificial Intelligence and Machine Learning. Understand how AI is a broader concept and ML is an application of AI.