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Top 20 AI Interview Questions

General Concepts in Generative AI:

  1. What is Generative AI, and how does it differ from Discriminative AI?
    1. Follow-up: Can you give examples of generative models and discriminative models?
  2. Explain the difference between Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
    1. Follow-up: In what situations would you prefer to use GANs over VAEs and vice versa?
  3. How does a Variational Autoencoder (VAE) work?
    1. Follow-up: Can you explain the role of the encoder, decoder, and the latent space in VAEs?
  4. What are GANs (Generative Adversarial Networks), and how do they work?
    1. Follow-up: What is the role of the generator and the discriminator in a GAN?
  5. Explain the concept of latent space in generative models.
    1. Follow-up: How does interpolation in latent space lead to generating new samples?

Technical and Theoretical Aspects of Generative AI:

  • How does the training process of GANs work, and what is the objective of each model (generator and discriminator)?
    • Follow-up: How do you avoid mode collapse in GANs during training?
  • What are the common challenges faced when training GANs?
    • Follow-up: How would you address issues like instability during training, mode collapse, and vanishing gradients in GANs?
  • What is the KL divergence, and how is it used in training Variational Autoencoders (VAEs)?
    • Follow-up: Can you explain the role of KL divergence in the VAE loss function?
  • What is a Transformer model, and how does it differ from traditional RNNs and CNNs in generating sequences (like text or images)?
    • Follow-up: What is the role of self-attention in the Transformer architecture?
  • Explain how the Attention mechanism works in generative models.
    • Follow-up: How does attention improve the generation of sequences compared to recurrent models?

Applications of Generative AI:

  1. How are GANs used in image generation, and what are some real-world applications of GANs?
    1. Follow-up: Can you discuss specific use cases like DeepFakes, image super-resolution, or style transfer?
  2. How can Generative AI be applied to text generation, such as in language models like GPT?
    1. Follow-up: How do models like GPT fine-tune to perform well on specific tasks like summarization, translation, or Q&A?
  3. What is the role of generative models in drug discovery or medical imaging?
    1. Follow-up: How can generative models aid in discovering novel molecules or generating synthetic medical data?
  4. Explain the difference between autoregressive models (e.g., GPT) and autoencoding models (e.g., BERT) in text generation.
    1. Follow-up: Why are autoregressive models more suited for tasks like text generation compared to autoencoding models?
  5. How can Generative AI models be used in music or art creation?
    1. Follow-up: What challenges do you face when generating music or artwork using AI, compared to image or text generation?

Advanced Topics in Generative AI:

  1. What are Diffusion Models in Generative AI, and how do they work?
    1. Follow-up: How do diffusion models compare with GANs in terms of training stability and output quality?
  2. What is the role of Reinforcement Learning in improving Generative AI models?
    1. Follow-up: Can you explain how RL can be integrated into models like GPT for tasks such as content generation or dialogue management?
  3. What are Energy-Based Models (EBMs), and how do they relate to Generative AI?
    1. Follow-up: What are the differences between EBMs and other generative models like GANs or VAEs?
  4. How do Contrastive Divergence and Maximum Likelihood Estimation relate to training generative models?
    1. Follow-up: How are these techniques applied in models like Boltzmann machines or other probabilistic generative models?
  5. What are the ethical challenges associated with Generative AI?
    1. Follow-up: How would you mitigate risks like misinformation (DeepFakes), bias in AI-generated content, or copyright issues in AI-generated media?

These questions test both the foundational knowledge of Generative AI and the practical aspects of applying these models to real-world problems. A candidate’s ability to understand and navigate the technical complexities, along with discussing relevant applications and challenges, is key to succeeding in a Generative AI interview.

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