Key Components of Generative AI

- Overview

Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Large Language Models (LLMs) are key components of generative AI, which uses algorithms and models to create new content that can imitate or surpass human creativity:

• Variational Autoencoders (VAEs): Capture data's underlying structure and can generate new samples by sampling from a learned latent space. VAEs can help with text generation and paraphrasing, and can enhance data augmentation techniques for Natural Language Processing (NLP) tasks. VAEs can also generate outputs like images faster than diffusion models, but the images aren't as detailed. VAEs are more commonly used in signal analysis.
• Generative Adversarial Networks (GANs): Pit two neural networks against each other: a generator that creates new examples and a discriminator that learns to distinguish between real and fake content. GANs are often used to generate multimedia, like voices and images, and can also facilitate style transfer in language translation and sentiment analysis.
• Large Language Models (LLMs): Power language translation services, text summarization, and conversational AI, which can improve user experiences in chatbots and virtual assistants. LLMs can also be used in industries like finance for tasks like fraud detection by analyzing textual data to identify anomalies. However, LLMs have been criticized for enabling academic dishonesty and potentially spreading misinformation because of their ability to generate convincing textual content.

[More to come ...]