Week 13 - Generative AI Fundamentals

What is Generative AI?

Generative AI enables users to quickly generate new content based on a variety of inputs. The input and output of these models can include text, images, sounds, animations, 3D models, or other types of materials.

How Does Generative AI Work?

Generative AI models use neural networks to identify patterns and structures in existing data to generate new and original content. One of the breakthroughs in generative AI models is the ability to train using different learning methods, including unsupervised or semi-supervised learning. This enables organizations to more easily and quickly build base models from large amounts of unlabeled data. As the name suggests, base models can be used as the basis for artificial intelligence systems that can perform multiple tasks.

How to Evaluate Generative AI Models?

The three key requirements for a successful generative AI model are:

Quality: Especially for applications that interact directly with users, having high-quality generated output is key. For example, in speech generation, poor speech quality is difficult to understand. Likewise, in image generation, the desired output should be visually indistinguishable from natural images.

Diversity: A good generative model can capture some patterns in the data distribution without sacrificing generative quality. This helps reduce unnecessary bias in the learning model.

Speed: Many interactive applications need to be generated quickly, such as real-time image editing to allow use in content creation workflows.

What are the Benefits of Generative AI?

1. Generative AI algorithms can be used to create new, original content such as images, videos, and text that is indistinguishable from content created by humans. This is useful for applications such as entertainment, advertising, and creative arts.
2. Generative AI algorithms can be used to improve the efficiency and accuracy of existing artificial intelligence systems, such as natural language processing and computer vision.
3. Generative AI algorithms can be used to explore and analyze complex data in new ways, allowing businesses and researchers to uncover hidden patterns and trends that are not apparent from raw data alone.
4. Generative artificial intelligence algorithms can help automate and accelerate various tasks and processes, saving time and resources for businesses and organizations.

Overall, generative artificial intelligence has the potential to have a significant impact on a wide range of industries and applications, and is an important area of ​​artificial intelligence research and development.