How Generative AI Works
Generative AI is a type of artificial intelligence that involves using algorithms to create new data that resembles training data. In this section, we'll explore the key concepts behind Generative AI and how it works.
Generative AI Overview
Generative AI is based on the idea of training an algorithm on a set of data, and then using that algorithm to generate new data that is similar to the training data. This is accomplished using techniques such as neural networks, which are composed of interconnected nodes that can process and analyze data.
There are several key concepts that are important to understand when it comes to Generative AI:
- Training Data: The data used to train the algorithm, which should be representative of the data that the algorithm will be generating.
- Generative Model: The algorithm that is trained on the training data to generate new data that is similar to the training data.
- Latent Space: A space that represents a lower-dimensional encoding of the input data, which can be used to generate new data.
- Loss Function: A function that is used to measure the difference between the generated data and the training data, and is used to adjust the generative model during training.
Generative AI Techniques
There are several techniques that are commonly used in Generative AI:
- Neural Networks: A type of algorithm that is designed to process and analyze data, and is often used in Generative AI to generate new data based on the input data.
- GANs: Generative Adversarial Networks are a type of neural network that consists of a generator and a discriminator, which work together to generate new data that is similar to the training data.
- VAEs: Variational Autoencoders are a type of neural network that can learn to encode and decode data, and can be used for tasks such as image generation and compression.