The Cherry Pick Ratio
If my feeds on LinkedIn and X are to be believed, AI models generate nothing but useful and incredibly high quality outputs. Unlikely to be the case. And, in fact, I don’t actually know how much effort is behind each generated image, video, song, or hilarious joke that is shared. There is something hidden in the way we talk about generative AI.
What do I mean? Well, there are two things that a generative model is trying to do. One is to learn patterns in all of the training data to create new high-quality outputs. This is the ‘hard’ science of generative AI: improving the model, data, and learning to generate higher quality outputs.
But, we also expect the models to accomplish a second, much more subjective task. It must translate some input from the user (e.g. a text prompt or an image) into an generated output that satisfies the user’s expectations. Sometimes the user knows what they want. Other times they will “know it when they see it”. Either way, the first output is rarely the final one. It could take ten, twenty, thirty generations before the model generates an acceptable output that they like. Or worse, before they get frustrated and give up.
This leads me to a question that is discussed often with peers, but is somewhat lacking in the literature around generative models. How hard are users willing to work to get to a final result?
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