Here’s a list of frequently asked questions (FAQs) covering Generative AI (GenAI)
What is Generative AI, and how does it work?
Generative AI refers to AI models that generate new data similar to their training data, often using algorithms like transformers, GANs, or diffusion models.
What are popular applications of Generative AI?
GenAI is widely used in content creation (text, images, audio), personalized recommendations, chatbots, code generation, and drug discovery.
How is Generative AI different from traditional AI?
Traditional AI typically focuses on classification or prediction, while Generative AI creates new, original content based on learned patterns.
What is prompt engineering, and why is it important?
Prompt engineering involves crafting specific inputs to get desired responses from GenAI models. It’s crucial for improving model accuracy and relevance.
What are the ethical concerns associated with Generative AI?
Concerns include privacy, data security, misinformation, bias, and accountability in automated content generation.
What are diffusion models, and how are they used in GenAI?
Diffusion models iteratively improve data by reducing noise, commonly used in generating high-quality images and videos.
How can businesses leverage Generative AI?
Businesses use GenAI for customer service automation, personalized marketing, creative content generation, data analysis, and process optimization.
What is Reinforcement Learning from Human Feedback (RLHF)?
RLHF is a method of fine-tuning AI by using human feedback to improve the quality and alignment of the model’s responses.
What is model fine-tuning, and when should it be used?
Fine-tuning is adapting a pre-trained model to specific data or tasks, ideal when needing to tailor GenAI for industry-specific applications.
What is “AI hallucination,” and how can it be minimized?
AI hallucination refers to GenAI generating incorrect or misleading information. It can be reduced by refining models and improving data quality.
