Difficulty in predicting outcomes of AI model outputs based on prompt changes

Signals over time

2 observed signals across 1 sources, tracked for 6 days. Confidence: low.

What people are saying Observed

• “> It's what you'd expect by posing "will AI do X by 2026?" and then flipping a coin to get an answer. I think that's technically correct but misleading here, because these aren't simple yes/no predictions. They are specific, relatively detailed predictions of what will happen. As an example, let's say you asked the questions: - Will AI kill everybody by 2026? - Will AI create FTL travel by 2026? - Will AI be able to travel back in time by 2026? - Will AI colonize Mars by 2026? ...and then flipped a coin. Flipping a coin would say "yes" to two of them, so you'd make these two predictions. But obviously, these were all bad predictions, so you'd actually end up with a 0% win ratio. Making detailed, specific long-term predictions and getting a 50% hit ratio means your predictions were reasonably good to begin with, since making speculative long-term predictions is incredibly difficult.”

  Hacker News · frustration

• “> Is Coding → Prompting like Assembly → High Level Coding? […] I do not agree with this simile. Compilers are, for the most part, deterministic in a way that current AI tools are not. It’s not quite about the determinism. It’s about being able to reason about the relationship between source code and compiled program with formal precision. You can predict which changes in the source code will lead to which exact changes in the behavior of the compiled program. The same isn’t the case about changes to an LLM prompt and the LLMs output. You could make an AI deterministic by fixing its source of randomness. That still wouldn’t allow you to reason about how its output will change when (for example) you add or remove a word in the prompt. The only way to find out is to run the LLM (= have the prompt run through the model and observe what comes out). That is the fundamental difference. Changes to source code have predictable and reason-able outcomes. You generally don’t have to compile the code and test it to know how precisely the change will affect the behavior of the compiled program according to the semantics of the programming language. That’s the case even if the compiler uses so”

  Hacker News · frustration