Generative AI in Content Creation

In the 50s, the idea of Artificial Intelligence first emerged when the scientist Alan Turing proposed the concept of a machine that could show intelligent behavior. Ever since, AI has made incredible progress, and at least 10 subcategories have been developed. Each subcategory has been categorized based on capabilities, functionalities, methods, and techniques, and application-specific AI. In this blog, we would like to talk about a special branch of AI that specializes in the generation of content based on patterns learned from data.

What is generative AI?

This branch is called Generative AI, also known as GenAI. To understand how this AI branch works, we need to look at one AI subcategory called Machine Learning. This subcategory trains systems to learn from data and make decisions and predictions based on patterns. Then, there is a subset of Machine Learning called Deep Learning, which uses a computer system modeled on the human brain and nervous system (Neural Networks) to learn and extract features from data, such as images, text, and audio. Finally, generative AI is a subset of Deep Learning that can generate text, images, audio, and videos.

Above all, Deep Learning has given Generative AI the capability to generate high-quality content by learning from existing datasets. More precisely, 3 Deep Learning architectures can be used to generate audio and videos:

• Autoregressive transformers generate content step-by-step, meaning that elements will be incorporated sequentially and the new outputs will build logically upon previous elements.
• Generative Adversarial Network (GAN) is composed of two elements: a generator and a discriminator. The generators create new content, while the discriminator reexamines how realistic the generated content is.
• Variational Autoencoder (VAE) is also composed of two parts: an encoder and a decoder. The encoder compresses content into simpler formats, while the decoder recreates content from the compressed audio or video.

Generative AI for audio generation

With the help of Generative AI, people can create any type of sound. It has been used to compose music, do remixes of existing songs, generate voiceovers for movies, audiobooks, or customer service agents, and has also powered some of the most well-known voice assistants: Siri and Alexa! This technology is mind-blowing, but... do you know how it works?

Furthermore, generating audio can be done through different techniques. For example, tokenization breaks audio into smaller units (tokens) that represent different features like pitch and rhythm. Then, quantization simplifies continuous audio signals into discrete values, similar to how large language models work. Finally, vectorization transforms audio data into a structured format that makes it easier for AI to find patterns and generate new audio.

Generative AI for video generation

On the other hand, Generative AI uses algorithms that can generate high-quality videos by learning from existing datasets. Needless to say, this technology has taken away the burden of getting equipment, finding actors, shooting, endless timelines, and the high costs of setting up the production.

Here, Natural Language Processing (NLP) comes into play by trying to understand the structure, intent, and emotion behind scripts, images, and audio, and generate corresponding visuals and audio. Additionally, 3D modeling can also be used to create realistic content like characters, objects, or landscapes.