Artificial Intelligence, or AI, is a fascinating field of computer science that focuses on creating intelligent machines capable of performing tasks that typically require human intelligence. These tasks include learning, reasoning, problem-solving, perception, and language understanding.

The journey of AI has seen significant evolution. Traditional AI, also known as rule-based or symbolic AI, relies on explicit programming and predefined rules to perform tasks. However, the limitations of this approach became evident as it struggled with complex, real-world scenarios.

So, here comes Generative AI, a new frontier in AI development. Unlike its predecessors, Generative AI is designed to learn from data and generate new content. This shift marks a departure from rule-based systems, as Generative AI can create original outputs based on patterns and examples it has encountered during training.

This blog aims to get into the core concepts of Generative AI and Traditional AI. By understanding the differences, we can appreciate how Generative AI's ability to generate content sets it apart from the more rule-centric approach of Traditional AI. 

Full Blog: Generative AI VS Traditional AI: Key Differences