In simple terms, generative AI systems are trained by exposure to vast data sets. They build and refine models based on this data. They learn to identify patterns – and to iterate and alter those patterns to create new content.
However, potential intellectual property issues arise at both the input and output stages: with the data an AI is trained on, and with the content it produces. Continue reading “Reading or copying? Searching for AI certainty in copyright law”
