1. How is Generative AI different from traditional machine learning?
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Machine learning analyzes existing data to identify trends and make predictions. Generative AI, in contrast, learns data patterns to create entirely new outputs that mimic real-world content, such as text, images, videos, or music.
2. Could you differentiate between supervised and unsupervised learning with examples?
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Supervised learning uses labeled datasets, e.g., predicting house prices from attributes. Unsupervised learning works with unlabeled data, such as clustering consumers based on shopping habits. Both solve data-driven problems differently.
3. What is the significance of Transformer architecture in Generative AI?
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Transformers employ self-attention mechanisms to capture long-range dependencies, enabling models like GPT to generate fluent, context-aware text or other sequential outputs.
4. What impact does overfitting have on AI models and how can it be minimized?
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Overfitting occurs when a model memorizes training data, harming performance on new data. Techniques like dropout, data augmentation, cross-validation, and regularization help improve generalization.
5. What are GANs and how do they work?
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GANs consist of a generator producing synthetic data and a discriminator evaluating its authenticity. Both networks compete, pushing the generator to produce increasingly realistic outputs.
6. What is feature engineering in machine learning?
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Feature engineering involves refining and creating data inputs to improve model performance. Tasks include scaling, encoding, and variable creation, which help the model recognize meaningful patterns.
7. Why is fine-tuning a pre-trained model useful in Generative AI?
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Fine-tuning adapts a pre-trained model to a specific domain using smaller datasets. It reduces training time and computational costs while improving accuracy by leveraging existing knowledge.
8. What is reinforcement learning and how is it applied?
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Reinforcement learning involves an agent learning by interacting with an environment and receiving rewards or penalties. It's used in robotics, gaming, autonomous systems, and recommendation engines.
9. How can the performance of generative AI outputs be evaluated?
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Evaluation combines automated metrics (BLEU, ROUGE for text) with human judgment of creativity and realism. Quantitative and qualitative assessments together provide accurate performance insights.
10. What are major challenges in deploying Generative AI and ML models?
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Deployment challenges include maintaining efficiency, managing computational costs, addressing bias, and protecting sensitive data. Continuous updates, monitoring, and optimization ensure responsible production use.
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