AI processes data through a series of computational steps that mimic human cognitive functions. The process begins with data collection, where AI gathers structured and unstructured data from various sources such as text, images, audio, and video. This raw data is then cleaned and preprocessed to remove inconsistencies, duplicates, and irrelevant information, ensuring that the AI system receives high-quality inputs.

Next, the AI model applies feature extraction, identifying important patterns, relationships, or characteristics within the data. These features serve as inputs for machine learning algorithms that learn from past examples. Traditional AI models use rule-based systems, whereas machine learning (ML) and deep learning (DL) models leverage statistical and neural network-based approaches to process data more effectively.

Once trained, the AI model undergoes inference, where it applies its learned knowledge to new, unseen data. The model generates predictions, classifications, or recommendations based on its training. Feedback mechanisms such as reinforcement learning or human-in-the-loop systems help refine the model over time, making it more accurate and reliable.

AI also utilizes natural language processing (NLP) for text analysis and computer vision for image recognition. Deep learning architectures like convolutional neural networks (CNNs) and transformers enable AI to process complex datasets with high precision.

Scalability and efficiency are enhanced using cloud computing and specialized hardware such as GPUs and TPUs, which accelerate computations. The integration of real-time data processing allows AI to make instant decisions in applications like self-driving cars and fraud detection.

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