Data gravity occurs when the volume of data in a warehouse increases and the number of uses also increases. In some cases, copying or moving data can be troublesome and expensive. Therefore, data tends to pull services, applications and other data into its warehouse. Primary examples of data gravity are data warehouses and data lakes. Data in these systems is inert. Scalable volumes of data often break existing infrastructure and processes, requiring risky and expensive fixes. Therefore, the best practice is to move design processing to data, not the other way around.
Model collapse refers to the permanent decline in performance of productive AI models when trained with the contents produced by previous AI models. This phenomenon is reminiscent of a widely accepted principle in AI development: a model is only as good as the data on which it is trained.
Data matching is the process of linking a data field from one source to a data field from another source.
Gesture Recognition is a technology that detects a user's physical movements (hand, arm, face, or body movements), transforming these gestures into digital commands.
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