Capital de TokensAI and humans: a machine for premature certainty
Watch on YouTube Generative models have a natural tendency to fill in gaps. It is part of what makes them fluent and pleasant to use. Faced with incomplete information, they produce an explanation that connects the dots. And we humans have our own tendency: we prefer a coherent story to admitting that we still do not
Generative models have a natural tendency to fill in gaps. It is part of what makes them fluent and pleasant to use. Faced with incomplete information, they produce an explanation that connects the dots. And we humans have our own tendency: we prefer a coherent story to admitting that we still do not know what is happening. When those two tendencies come together, a machine for premature certainty emerges. That is why, after asking AI to connect variables, we should make a second, much more uncomfortable request: “Give me three alternative explanations that would make this conclusion false. Specify what data we would observe if each were true.” The aim is not to paralyze us, but to design better tests. The useful question is not “What is the cause?” In many real-world problems, we cannot know yet. The question is: “Which explanation deserves a small, inexpensive, and sufficiently informative experiment?”
Full episode: https://youtu.be/s-rLknx2Cn0
🤖 AI-generated content: the script, voices, and images in this episode were produced using artificial intelligence tools.
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