General Concepts in Generative AI:
- What is Generative AI, and how does it differ from Discriminative AI?
- Follow-up: Can you give examples of generative models and discriminative models?
- Explain the difference between Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).
- Follow-up: In what situations would you prefer to use GANs over VAEs and vice versa?
- How does a Variational Autoencoder (VAE) work?
- Follow-up: Can you explain the role of the encoder, decoder, and the latent space in VAEs?
- What are GANs (Generative Adversarial Networks), and how do they work?
- Follow-up: What is the role of the generator and the discriminator in a GAN?
- Explain the concept of latent space in generative models.
- 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:
- How are GANs used in image generation, and what are some real-world applications of GANs?
- Follow-up: Can you discuss specific use cases like DeepFakes, image super-resolution, or style transfer?
- How can Generative AI be applied to text generation, such as in language models like GPT?
- Follow-up: How do models like GPT fine-tune to perform well on specific tasks like summarization, translation, or Q&A?
- What is the role of generative models in drug discovery or medical imaging?
- Follow-up: How can generative models aid in discovering novel molecules or generating synthetic medical data?
- Explain the difference between autoregressive models (e.g., GPT) and autoencoding models (e.g., BERT) in text generation.
- Follow-up: Why are autoregressive models more suited for tasks like text generation compared to autoencoding models?
- How can Generative AI models be used in music or art creation?
- 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:
- What are Diffusion Models in Generative AI, and how do they work?
- Follow-up: How do diffusion models compare with GANs in terms of training stability and output quality?
- What is the role of Reinforcement Learning in improving Generative AI models?
- Follow-up: Can you explain how RL can be integrated into models like GPT for tasks such as content generation or dialogue management?
- What are Energy-Based Models (EBMs), and how do they relate to Generative AI?
- Follow-up: What are the differences between EBMs and other generative models like GANs or VAEs?
- How do Contrastive Divergence and Maximum Likelihood Estimation relate to training generative models?
- Follow-up: How are these techniques applied in models like Boltzmann machines or other probabilistic generative models?
- What are the ethical challenges associated with Generative AI?
- 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.