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Generative AI (Gen AI)

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The term generative AI gets it’s name from the fact that it uses artificial intelligence to generate new content. Text, images, music, video and computer code can be generated, but the material is typically a response to a prompt from a user. A prompt is an instruction or request given to an AI system designed you elicit a type of response from it. To illustrate, I might ask a generative AI tool to write a short story about an adventurous and mischievous koala.

Generative AI is possible because of machine learning, which allows computers to ‘learn’ by analysing the patterns and relationships in data. The data an AI system has ‘learned’ from is called training data. These large datasets of existing content sit behind one of the biggest criticisms of generative AI; that the training data for many AI systems was used without the permission or knowledge of the copyright owners of that material.

AI outputs are original material, but they draw on characteristics of the data they were trained on.

You may have noticed I don’t use the words ‘create’ or ‘make’ when I talk about what AI produces. While these definitionally apply to AI outputs, they are too often conceptually aligned to the notion of creativity which connotes the use of imagination to create something which is something we attribute to humans. Generative AI challenges notions of originality and human authorship.

I also talk about generated AI content as representations of things, rather than being that thing. What do I mean? Let me use an example: if I ask an AI tool to generate a photo of a cat what I get back is not a photo, but rather a representation of a photo. And the subject-matter of that not photo is not a cat, but actually the AI system’s approximation of what it understands a cat to be based off at the data available to it during the ML process.

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