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What are batch, incremental, on-line, off-line, deterministic, stochastic, adaptive, instantaneous, pattern, constructive, and sequential learning?
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What is model interpretability, and why is it important?
What are the limitations of AI in cybersecurity?
What frameworks can you use for ethical AI development?
What measures can ensure the robustness of AI systems?
Explain the difference between data bias and algorithmic bias.
Explain the concept of SHAP and its role in XAI.
How does human feedback improve AI models?
How do Generative AI models create synthetic data?
What are the limitations of current Generative AI models?
Why is data considered crucial in AI projects?
Explain the risks of adversarial attacks on AI models.
How do biases in AI models amplify existing inequalities?
What are the apis outside tensorflow project?
Provide examples of industries where fairness in AI is particularly critical.
How can you optimize AI models for edge deployment?
