GENERATIVE AI QUESTIONS WITH ANSWERS

Basics of Generative AI

1. What is Generative AI?

Generative AI refers to a type of artificial intelligence that can generate new data, such as text, images, audio, or code, based on the patterns it has learned from the training data.

2. How does Generative AI differ from traditional AI?

Traditional AI focuses on classification, prediction, and decision-making, whereas Generative AI creates new content or data similar to its training set.

3. What are some applications of Generative AI?

Applications include natural language generation (e.g., chatbots), image synthesis, code generation, music composition, and drug discovery.

4. What are GANs?

GANs (Generative Adversarial Networks) are a class of machine learning frameworks where two neural networks (generator and discriminator) compete to improve the quality of generated data.

5. What is the difference between GANs and VAEs?

GANs generate data using a generator-discriminator approach, while VAEs (Variational Autoencoders) use probabilistic methods to encode and decode data, emphasizing data distribution.

Technical Questions

6. What are the components of a GAN?

GANs consist of a Generator (creates synthetic data) and a Discriminator (evaluates the authenticity of the data).

7. What is the role of the loss function in Generative AI models?

The loss function helps optimize the model by reducing the difference between the generated output and the expected data.

8. What is prompt engineering in language models?

Prompt engineering is the process of crafting specific input prompts to guide a generative language model to produce desired outputs.

9. Explain the difference between GPT and BERT.

GPT (Generative Pre-trained Transformer) is a unidirectional model focused on text generation, while BERT (Bidirectional Encoder Representations from Transformers) is a bidirectional model designed for understanding and classification tasks.

10. What are transformers in Generative AI?

Transformers are neural network architectures that use self-attention mechanisms to process sequential data, making them efficient for language and sequence modeling.

Advanced Concepts

11. How does reinforcement learning improve Generative AI models?

Reinforcement Learning with Human Feedback (RLHF) fine-tunes models to align their outputs with human preferences, improving quality and relevance.

12. What are diffusion models in Generative AI?

Diffusion models are probabilistic methods that generate high-quality images by iteratively denoising random noise.

13. What is the significance of the latent space in generative models?

The latent space represents a compressed encoding of input data, enabling models to manipulate and generate similar but novel data.

14. What are the challenges in training GANs?

Challenges include mode collapse (limited diversity in output), unstable training, and difficulty in balancing the generator and discriminator.

15. How do zero-shot and few-shot learning relate to Generative AI?

Zero-shot learning enables models to perform tasks without specific training, while few-shot learning allows them to adapt with minimal examples.

Practical Applications

16. How are Generative AI models trained for text generation?

They are trained on large text datasets using transformer architectures, focusing on predicting the next word in a sequence.

17. What is the role of pre-training and fine-tuning in large language models?

Pre-training helps the model learn general patterns from large datasets, and fine-tuning adapts it for specific tasks or domains.

18. How do image generation models like DALL·E work?

DALL·E uses a transformer architecture to generate images from textual descriptions by learning text-image relationships.

19. What are the ethical concerns in Generative AI?

Concerns include bias, misinformation, copyright infringement, and misuse for creating deepfakes or harmful content.

20. How is Generative AI used in video synthesis?

It generates video content frame by frame using models like GANs or diffusion-based techniques, trained on motion and style patterns.

Trends and Future Prospects

21. What is fine-tuning in the context of generative models?

Fine-tuning involves adapting a pre-trained model to a specific task or domain by training it on a smaller, task-specific dataset.

22. What are ethical AI principles for Generative AI?

Principles include fairness, transparency, accountability, privacy protection, and minimizing harm.

23. What advancements are driving the growth of Generative AI?

Key advancements include improved transformer models, larger datasets, better hardware, and innovations in self-supervised learning.

24. What are token embeddings in Generative AI models?

Token embeddings are vector representations of input data (e.g., words or pixels) that models use to understand relationships and context.

25. What is the future of Generative AI?

Generative AI is expected to evolve with better efficiency, multimodal capabilities, personalized outputs, and real-world applications like healthcare and education.