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Related Questions
- How do machine learning algorithms currently struggle to capture nuances of human language, such as sarcasm, idioms, and figurative language?
- What are some open challenges in developing machine learning models that can handle the complexity of human language, including context, ambiguity, and implicit meaning?
- How can machine learning algorithms be improved to better understand the subtleties of human language, such as tone, intent, and emotional context?
- What role do linguistic and cognitive biases play in the development of machine learning models that process human language?
- How can machine learning models be designed to handle the vast range of languages, dialects, and cultural nuances that exist across the globe?
- What are some strategies for addressing the issue of overfitting in machine learning models that are trained on human language data?
- How can machine learning models be made more robust to handle the variability and uncertainty that is inherent in human language?
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