Скачать или смотреть AI Systems: Input A ≠ Output B. 🛑 (Part 2)

AI Systems: Input A ≠ Output B. 🛑 (Part 2)

This is Part 2 of a 6-part series on AI testing.

In this video, I break a common myth:
“AI models are predictable, so they can be tested like traditional software.”

They can’t.

Traditional software assumes stable behaviour.
Input A produces Output B.

AI systems behave differently.

The same prompt can produce different outputs over time due to:
• model updates
• new data
• feedback loops

If testing strategies assume stability, they will fail.

This is why AI testing must move beyond reactive debugging and adopt proactive monitoring.

That includes:
• Guardrails to prevent data leaks, bias, and GDPR violations
• Continuous monitoring to detect behaviour drift once systems are live

📂 AI Testing Series Index

• Part 1: Separate AI Testing from AI Acceleration
   • AI Acceleration ≠ AI Testing  

• Part 2: Why Input A ≠ Output B (this video)

• Part 3: Use AI-Specific Testing Tools (Not Traditional Test Runners) 🔓
(Unlocks at 50 reposts)

Action:
Repost this to unlock Part 3, where we move from theory to tooling.

Hashtags:
#AITesting #ResponsibleAI #SoftwareTesting #MLMonitoring #AIEngineering #GDPR #TrustworthyAI