This article explains the difference between generative AI vs AI in general.

--

Generative AI is a subset of Artificial Intelligence (AI) that focuses on creating new content, rather than simply analyzing or interpreting existing data.

AI in general refers to any system that can perform tasks that would normally require human intelligence, such as learning, reasoning, problem-solving, and perception. This encompasses a wide range of technologies, from simple rule-based systems to complex neural networks.

Generative AI specifically uses machine learning models to generate new data, such as text, images, music, or code. It learns patterns from existing data and then uses that knowledge to create something original.

Here's a breakdown of the key differences:

Focus: AI focuses on a broad range of tasks, while generative AI is specifically focused on content creation.   Process: AI can use various techniques, while generative AI often relies on deep learning models.   Output: AI can produce various outputs, while generative AI produces new content.

Term	Definition
Artificial Intelligence (AI)	A broad field of computer science that aims to create intelligent agents, which are systems that can reason, learn, and act autonomously.
Machine Learning (ML)	A subset of AI that focuses on algorithms that allow computers to learn from data and improve their performance on a specific task without being explicitly programmed.
Generative AI	A type of AI that uses machine learning models to generate new content, such as text, images, or music.
Business Intelligence (BI)	The use of software and technologies to collect, analyze, and present data to help businesses make informed decisions.
Data Engineering	The process of designing, building, and maintaining systems that store, manage, and process large datasets for use in analytics and machine learning applications.