Transcript
0:00 Have you observed the failure with error analysis? So, what does that mean? That means either it's occurring naturally in the data or you have a strong suspicion that this is something that can occur and you have done the due diligence to simulate the error. If you can't simulate the error, then this probably has low ROI. When you do error analysis or when you're looking at your data, you'll find all kinds of problems. Some of them will be very obvious to you, but if there's only little iteration, it's not really clear whether you need an eval. But if it's like, "Hey, you know what? This is a very tricky problem.
0:31 Like, oh, the medical AI assistant is assigning the wrong drug despite, you know, having the right context. I don't really know why that happened. And also, it's a impactful error." So, it gives you a high value. That's where you know that this eval is going to pay off because then now you can have something to iterate against and it's also worth iterating against the error. It's important and you need something to help you make progress.
Summary
- Error analysis helps identify problems in data, either naturally occurring or suspected.
- Simulating errors is essential; if it can't be done, the ROI may be low.
- Obvious issues may not require evaluation if they involve minimal iteration.
- High-impact errors, such as incorrect drug assignments by AI, necessitate thorough evaluation.
- Understanding the context of errors aids in prioritizing which evaluations to pursue.
- Iterating against identified errors is vital for making progress and improving systems.