AI vs. Machine Learning: What’s the difference?

Artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but they are distinct concepts. AI is the broader field, encompassing any technology that enables machines to perform tasks that typically require human intelligence. Machine learning, on the other hand, is a subset of AI that focuses on systems that learn from data and improve over time without explicit programming.

AI includes rule-based automation, deep learning, and generative AI, while ML specializes in self-learning algorithms. For example, chatbots use ML to enhance responses over time, whereas robotic process automation (RPA) follows predefined rules. Understanding this difference is crucial for businesses looking to integrate AI-powered solutions effectively.

Key takeaways:

• AI is the broader concept, while ML is a subset of AI.

• ML focuses on data-driven learning and algorithmic improvements.

• AI includes robotics, automation, NLP, and deep learning.

• ML is used in recommendation engines, fraud detection, and AI-driven automation.