Is good enough really good enough ?

Is good enough really good enough ?

Is 90% accuracy good enough for AI? Not when lives are at stake. So here is what to do about it.

Imagine a pilot landing your plane correctly only 9 out of 10 times. Terrifying, right? đŸ˜±

When it comes to AI in critical applications, near-perfect performance isn't just a goal—it's a necessity.

Here's why 99.9% accuracy matters:

  • In healthcare: Misdiagnosis can be fatal
  • In finance: Small errors can cost millions
  • In autonomous vehicles: Any mistake is a potential accident

Actually very few use cases would need 90% accuracy.

And still, this the perf of most of our current "LLM".

They're impressive, but quite fallible.

That's why we need to be strategic about AI deployment:

  1. Use AI in creative applications where there is no "ground truth"
  2. Pair AI with reliable traditional systems for critical tasks
  3. Maintain human oversight in high-stakes scenarios

The future of AI isn't just about being good. It's about being perfect.

And that's why the future of AI is probably not about what current LLM are.

What's your take? What level of accuracy do you expect in your use cases ? đŸ€”


Text drafted through a chat with an AI model. Visual: Stable Diffusion derivative. Edits by Jean-Paul Paoli. Reach out if you believe part of this content infringes on copyrighted material