A discussion on Reddit's r/MachineLearning subreddit questions the current relevance of theoretical guidance in machine learning practices. The original poster recalls a time when established theories like avoiding overfitting, the limitations of large models due to data scarcity, and the benefits of ensemble methods were foundational. However, recent empirical successes have overturned many of these principles, leading to confusion as the theoretical underpinnings were not formally retracted. The community is debating whether any theoretically-backed practices still hold sway or if the field has become entirely empirical, driven by what appears to work in practice. AI
IMPACT Raises questions about the future direction of AI development and the balance between theoretical foundations and empirical results.
RANK_REASON Discussion on a subreddit questioning established practices in a field.
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