What are generative models?

Generative models are a type of machine learning model that can create new data instances similar to the training data. They learn the underlying patterns and structure of the data and then use this knowledge to generate new samples.

What are the main differences between GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders)?

GANs consist of two networks, a generator and a discriminator, that compete against each other. VAEs use an encoder-decoder architecture and learn a latent representation of the data. GANs often produce sharper images, while VAEs offer better latent space control.

Explain the basic idea behind diffusion models.

Diffusion models gradually add noise to an image until it becomes pure noise. Then, they learn to reverse this process, removing noise to generate a new image. They are known for producing high-quality samples and offer flexibility in controlling the generation process.

What is FID (Fréchet Inception Distance) and how is it used to evaluate generative models?

FID is a metric that measures the similarity between real and generated images by comparing their feature distributions. Lower FID scores indicate better quality and diversity in generated images.

Name three applications of generative AI in text generation.

1. Language Models (e.g., GPT-3)
2. Dialogue Systems (e.g., chatbots)
3. Creative Writing (e.g., poetry, stories)

Explain how bias can manifest in generative models and its potential consequences.

Generative models can inherit biases present in the training data, leading to the generation of discriminatory or harmful content. This can perpetuate stereotypes, spread misinformation, and cause harm to marginalized groups.

What are some key considerations for responsible AI development in the context of generative AI?

1. Ensuring diverse and representative training data
2. Implementing fairness constraints and bias mitigation techniques
3. Developing transparent and explainable models
4. Establishing clear guidelines for the use and deployment of generated content