What is Generative AI?

Generative artificial intelligence (generative AI, GenAI, or GAI) is a branch of artificial intelligence that uses machine learning algorithms to create new and meaningful content, such as text, images, audio, or video. Unlike traditional AI methods, generative AI not only performs analytical tasks but also creatively generates content. Its outputs are increasingly indistinguishable from human-created works.

1. Basic Principles of Generative AI

Generative AI is based on generative models, whose core goal is to create new data samples by learning patterns from training data. For example, in text generation tasks, generative AI models like OpenAI's GPT-4 learn from extensive language data to produce coherent and grammatically correct text. In image generation tasks, models such as DALL-E 2 or MidJourney can create images based on textual input. These models work by understanding the distribution of training data to generate new content similar to it.

2. Technical Foundations of Generative AI

The core technologies of generative AI are based on various model architectures, particularly deep learning models. Some key technologies include:

• Generative Adversarial Networks (GANs): By pitting two neural networks against each other, GANs can generate realistic data samples and are widely used in image and video creation.

• Variational Autoencoders (VAEs): These models learn data distributions through an encoder-decoder structure and are used to generate new samples, commonly for images and sounds.

• Autoregressive Models and Transformers: Models like the GPT series use an autoregressive approach to generate text, training on large-scale data to capture long-range dependencies within text.

3. Applications of Generative AI

• Applications in Education

• Applications in Healthcare