Generative
AI is a branch of artificial intelligence that is used to generate new text,
videos, audio, images, code, or other synthetic data. The technology was
initially introduced to automate repetitive tasks in digital audio and image
correction.
How does Generative work?
Generative
AI algorithm is trained using a neural network, through which it identifies
patterns and structures in the existing data. The objective behind Gen AI is to
create new data.
Currently,
there are two of the most widely used Gen AI models.
- Generative Adversarial Networks (GANs): GANs are a class of machine learning models that are used to generate new data samples that resemble a given training dataset. They were first introduced by Ian Goodfellow and his colleagues in 2014.GAN consists of two major components: a generator and a discriminator. The generator generates synthetic data such as text, video or images, while discriminator task is to identify real and fake samples.
- Transformer-Based Models: These models follow the concept of encoder and decoder based architecture. It works on the concept of "Attention is All You Need," a paper published by Google in 2017.
Applications of Generative AI
- Text Generation: Gen AI platforms such as ChatGPT are used to generate text for articles, blogs, and content creation for marketing.
- Code Generation: Code completion, code generation, test case generation, bug fixing, model integration
- Visual Content: Image Generation and Enhancement, Video Creation, 3D Shape Generation
- Audio Generation: Creating Music, Text-to-Speech Generators, and Speech-to Text Convertors
