How AI store information

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  • Data Representation: AI uses various formats to represent data, such as vectors, matrices, and tensors. For example, in natural language processing, words might be represented as vectors in a high-dimensional space.

  • Neural Networks: In models like neural networks, information is stored in the weights and biases of the connections between neurons. During training, these weights are adjusted based on the input data and the desired output.

  • Databases: AI systems often use databases to store structured data. This can include relational databases (SQL) or non-relational databases (NoSQL) that allow for flexible data storage.

  • Memory Mechanisms: Some AI models, like recurrent neural networks (RNNs) and transformers, have specific mechanisms to maintain and access information over time, allowing them to handle sequences and context.

  • Training Data: The knowledge of an AI model is derived from the training data it processes. This data is stored in various forms during the training phase and can be accessed for making predictions.