Artificial Intelligence regarding doughts

[AI tradition vs AI Modern]

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Hey @Unaiza, Welcome to the community!

I will try briefly make it clear for you the difference between “Traditional AI” and “Modern AI”.

First Let’s start with Traditional AI: It refers to the early approaches and methods in the field of artificial intelligence that relied on symbolic reasoning, rule-based systems, and manually designed features. So we basically involve encoding human knowledge into a set of predefined rules to enable computers to perform specific tasks or make decisions within narrow domains.

On the other hand Modern AI: Encompasses contemporary approaches and techniques that heavily rely on machine learning, deep learning, and data-driven methods. It involves training algorithms on large datasets to learn patterns, features, and representations directly from the data. It also can generalize their learning to perform well across diverse tasks and domains.

Imagine Modern AI as the present moment, encompassing the current state of AI applications. Within this landscape, we find pivotal elements such as deep learning, neural networks, and methodologies like transfer learning and reinforcement learning are integral components of modern AI, allowing for more autonomous and adaptive learning systems that can handle complex tasks such as image recognition, natural language understanding, and decision-making.


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