The Mystique of AI Training

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The training process for AI involves iterating through a simple math problem to reduce errors and increase accuracy.
Training AI is not as mystifying as it may seem, as it primarily involves running an algorithm repeatedly.
Optimizing AI models requires finding the ideal parameters that minimize errors or loss.
Supervised learning, which uses labeled examples, is a common approach in AI and is still widely used in industry.

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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