1/ A recent controversy at Google has sparked important questions about training ML models on the output of other models. Let's dive into the engineering, business, and legal aspects of this practice. Buckle up, folks! 🧵

2/ Engineering recipes for training algorithms on generated data are still evolving. Instances of using a competitor's model outputs to train your own are surfacing. Are these techniques fair game or should there be limits?

3/ Business-wise, data may not always make your business more defensible. Market leaders might spend resources gathering data, but if their product's data makes it easier for competitors to catch up, is that initial effort a strong defense?

4/ Legally, OpenAI’s terms of use forbid using their output to develop competing models. But what if someone scrapes text from ShareGPT that they didn't generate? And are such terms enforceable in light of antitrust & fair-use laws?

5/ Generative AI offers creative use cases for training models using others' outputs. As we explore these exciting trends, it's crucial to navigate the legal & ethical landscape responsibly. Stay tuned for more developments! #ResponsibleAI

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