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Many error analysis processes work best when the system automates something humans can do, because human-level performance can be used as a benchmark. If the final output or intermediate components do things humans cannot do well, some procedures do not apply.

Human-Solvable Problems Enable More Powerful Error Analysis Tools

Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects.
https://www.deeplearning.ai/machine-learning-yearning/

Note: The content of the book is aligned with the Coursera Deeplearning.ai specialization.  https://www.deeplearning.ai/deep-learning-specialization/ 

Machine Learning Yearning (Deeplearning.ai)

Why do error analysis processes work best when an ML system automates a human-solvable task?

When an ML system automates a task humans can do well, human-level performance can serve as a benchmark for error analysis.

Working on human-solvable problems provides more powerful _____ tools, enabling more efficient team prioritization.

Match each task characteristic to its implication for applying error analysis procedures.

Order the steps for applying human-level benchmarking in error analysis for a human-solvable ML task.

An ML team builds a system performing a task no human expert can reliably evaluate. What should the team expect about error analysis?

All standard error analysis procedures in Machine Learning Yearning apply equally regardless of whether humans can perform the task.

If an ML system's final output or intermediate components do things _____ cannot do well, some error analysis procedures will not apply.

Match each ML development scenario to its consequence for error analysis tooling.

Order the reasoning steps explaining why human-solvable problems lead to more efficient team prioritization in ML development.

Question:
Explain why error analysis processes are more powerful when an ML system automates a human-solvable problem. How does this impact a development team's ability to prioritize their work efficiently?

Sample answer:
When an ML system automates a task that humans can do, developers can use human-level performance as a benchmark to evaluate the system. This benchmarking allows the team to pinpoint exactly where the system is falling short compared to a human. With these powerful error analysis tools, the team can clearly identify which components or specific errors are causing the most significant performance gaps, thus allowing them to prioritize their work efficiently on the most impactful areas.

Key points:
- Automating human-solvable tasks allows benchmarking against human-level performance.
- Human-level benchmarking provides a clear reference point for evaluating system output.
- This results in more powerful and applicable error analysis tools.
- Better error analysis allows teams to identify the most critical areas for improvement.
- Consequently, teams can prioritize their development work more efficiently.

Rubric:
The essay should clearly connect human-solvable problems to the availability of human-level performance benchmarks. It must explain how these benchmarks serve as powerful error analysis tools and conclude by linking these tools to the team's ability to efficiently prioritize their work.

Assess the value of human-level benchmarks in prioritizing ML team tasks

Case context:
Your ML team is building a system to predict the complex folding structures of newly discovered proteins, a task that no human expert can perform accurately or consistently.

Question:
Based on the nature of this task, what limitations should your team expect regarding error analysis, and why will some standard procedures not apply?

Sample answer:
Because the team is working on predicting protein structures—a task humans cannot do well—they should expect that many standard error analysis processes will not apply. Specifically, they will lack a human-level performance benchmark to compare their final output or intermediate components against. This means they will not have the powerful error analysis tools that are typically available for human-solvable problems, making it harder to efficiently prioritize the team's work.

Key points:
- The task involves something humans cannot do well.
- The system lacks a human-level performance benchmark.
- Many standard error analysis procedures will not apply without this benchmark.
- The team will miss out on powerful error analysis tools, reducing their ability to efficiently prioritize work.

Rubric:
The response must recognize that the task is not human-solvable, identify the lack of a human-level performance benchmark, and explain that without this benchmark, standard error analysis procedures and efficient team prioritization will be hindered.

Diagnosing error analysis limitations in a novel ML application

Question:
Briefly explain why some error analysis procedures do not apply to ML systems where intermediate components perform tasks humans cannot do well.

Sample answer:
Error analysis procedures often rely on human-level performance as a benchmark. If intermediate components perform tasks humans cannot do well, this benchmark is unavailable, rendering those specific error analysis procedures inapplicable.

Key points:
- Error analysis often benchmarks against human-level performance.
- Tasks humans cannot do well lack this human benchmark.

Rubric:
The answer should mention the reliance on human-level benchmarks and state that the inability of humans to perform the task removes the benchmark needed for certain error analysis procedures.

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