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Explain how human intuition improves underperforming ML models.
Question: Discuss how human intuition can be used during error analysis when a machine learning algorithm's performance is below human-level, using the speech recognition example from Machine Learning Yearning to illustrate your point.
Sample answer: When an algorithm performs worse than humans, developers can use error analysis to understand what specific information a person uses to arrive at the correct answer. For example, if a speech recognition algorithm confuses "pair" and "pear" in "a pair of apples," developers can rely on human intuition about context (knowing apples come in pairs) to understand the failure. This knowledge is then used to modify the learning algorithm so it can capture the same contextual clues that humans use naturally.
Key points:
- Algorithm is worse than human-level
- Understand information humans use to get the right answer
- Modify the learning algorithm
- Speech recognition example involving context of 'pair of apples'
Rubric: A good answer will identify the condition (algorithm is worse than human level), explain the mechanism (understanding what information humans use), and apply the speech recognition example correctly.
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Machine Learning
Deep Learning
Supervised Learning
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Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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