Being confidently wrong is the only thing holding AI back
2 days ago
- #Enterprise AI
- #AI Accuracy
- #Confident Inaccuracy
- Humans' usefulness stems from their ability to build context, learn from failures, and improve with practice, not just raw intelligence.
- Confident inaccuracy in AI systems leads to significant problems, including a universal verification tax, eroded trust, hidden failure modes, and compounding errors.
- AI adoption struggles, with reports indicating 90% of initiatives stuck in pilot mode and 95% of pilots failing.
- Accuracy in AI is critical; even small inaccuracies can lead to frequent errors in multi-step workflows.
- A usable AI system doesn't need perfect accuracy but should signal uncertainty and improve over time.
- An accuracy flywheel can be created by AI systems signaling uncertainty, receiving human input, and improving based on feedback.
- Challenges to AI accuracy include messy, stale, or unannotated data and procedural semantics known only to humans.
- Key questions for AI investments: Does the system signal uncertainty and learn from corrections?
- Effective solutions involve generating domain-specific plans and continuously specializing AI to the domain to improve accuracy and confidence.
- Building a system that leverages domain knowledge to calibrate confidence in generated plans is crucial for enterprise AI usability.