Cartography of generative AI

"01. Generative AI tools are used to automate tasks such as writing or generating images. This automation is achieved not by programming the concrete steps to be taken, but by using examples. If we have many examples of a case, we can process them using statistical networks that configure themselves by analysing their recurring patterns. Whether it is words, pixels or sound frequencies, we can obtain a statistical model by analysing and exploring a training data set."

"We could say that generative AI tools disassemble language (visual, textual) to reassemble it based on the calculation of probabilities. If until a few years ago these tools were trained to produce concrete expressions (the image of a face, text in a certain style), they now go beyond specific concreteness to produce many types and styles of content. This ability to generalise is based on the processing of much larger and more heterogeneous datasets in order to respond to all kinds of prompts."

Generative AI automates tasks like writing and image creation by learning from many examples rather than explicit programming. Statistical networks detect recurring patterns in words, pixels and sound frequencies to build probabilistic models. These models disassemble and reassemble visual and textual elements to generate content. Recent models generalise across many styles by training on much larger and more heterogeneous datasets. The expansion of generative capabilities has accelerated new economic activity and increased reliance on complex ecosystems. Industrial dataset compilation commonly relies on automated scraping of online content shared by millions of internet users.

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