Architecture of AI (Artificial Intelligence) that refers the component of AI enable an AI system to perceive inputs, process information, make decisions, and execute the produce outputs

Components of AI Architecture

Input data which collected form user in various ways like databases, sensors or images, then undergoes preprocessing, where it is cleaned, transformed, and relevant features are extracted to improve its quality. After that processed data sending to AI/ML Models. Then system learns patterns and makes decisions based on the training it has received. The trained model performs prediction on collated data to generate results. These predicted results are presented such as classifications, recommendations, or forecasts. Finally, to improve accuracy of AI system evaluates the model performance using new data and user feedback

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Flow of AI

Input Data → Preprocessing → AI/ML Model → Inference/Prediction → Output Result → Feedback & Continuous Learning.

Applications

  • Fraud detection

  • Autonomous vehicles

  • Virtual assistants (chatbots and voice assistants)

  • Image and speech recognition

  • Medical diagnosis

  • Recommendation systems

Generative AI( example :Chat-GPT)

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Three-Layer AI Architecture

  • Data Layer

Data collection and storage.

  • Model Layer

    • Training and learning algorithms.

  • Application Layer

    • User applications such as chatbots, autonomous systems and recommendation systems.

Key Technologies

  • Large Language (LLMs) Models

  • Deep Learning

  • Neural Networks

  • Transformers

Attention Mechanisms

Summary

To Summary architecture of AI is a structured framework consisting of raw data collection, preprocessing, model training, decision-making, output generation, and feedback. Modern AI systems such as Chat-GPT mainly built on deep learning architectures.