Capsule Networks and Education Targets
Machine Learning Street Talk (MLST)
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The Metrics Dilemma: Balancing Optimization and Innovation
This chapter explores the complexities and pitfalls of using metrics as targets in social sciences and economics. It critiques over-optimization and its impact on education and innovation, advocating for cognitive flexibility and the importance of exploration beyond rigid numerical goals. Through historical examples and discussions on standardized testing and governance, the chapter emphasizes the need to balance structured objectives with the freedom to innovate.
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