Generative Models: Autonomous Foundations

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Current generative models are perceived as inherently valuable, capable of producing outputs without the need for additional fine-tuning. These models operate by processing a sequence of input information and generating a corresponding completion, highlighting their versatility beyond just text. For instance, they can also be applied in creative fields, such as music, where a simple input like a few piano notes can lead to a fully generated composition.

Chris and Daniel take a step back to look at how generative AI fits into the wider landscape of ML/AI and data science. They talk through the differences in how one approaches “traditional” supervised learning and how practitioners are approaching generative AI based solutions (such as those using Midjourney or GPT family models). Finally, they talk through the risk and compliance implications of generative AI, which was in the news this week in the EU.

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