<ul><li>Bias in AI can result in unfair or inaccurate outcomes and addressing biases is crucial for fairer systems.</li><li>Limited generalization of AI models in real-world scenarios poses challenges in critical applications like autonomous vehicles or medical diagnostics.</li><li>Techniques such as unsupervised domain adaptation and data augmentation are being developed to bridge the gap between training and application domains.</li><li>Self-supervised learning (SSL) enables models to learn from unlabelled data, addressing generalization issues and reducing reliance on labeled data.</li><li>Robustness is crucial for AI systems in real-world applications to maintain reliable performance and build trust in their effectiveness.</li></ul>

Exploring the Challenges of AI in Real-World Applications

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