Machine Learning and AI

Machine Learning (ML) and Artificial Intelligence (AI) are closely related fields, often used interchangeably, but they have distinct definitions:

Artificial Intelligence (AI)

AI refers to the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. It involves creating algorithms to enable machines to perform tasks that would normally require human intelligence. These tasks include problem-solving, understanding natural language, recognizing patterns, and learning from experience. AI is the overarching discipline that encompasses everything from robotic process automation to actual robotics.

Machine Learning (ML)

ML is a subset of AI and is based on the idea that systems can learn from data, identify patterns, and make decisions with minimal human intervention. It involves the development of algorithms that can learn and make predictions or decisions based on data. The learning process is automated and improves as the system is exposed to more data. ML is often divided into categories such as supervised learning (learning from labeled data), unsupervised learning (learning from unlabeled data), and reinforcement learning (learning based on feedback from interactions with an environment).

In summary, AI is the broader concept of machines mimicking human intelligence, and ML is a specific approach within AI that teaches a machine how to learn from data. ML is one of the most effective ways to realize AI.

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