What is generative AI?

As of February 2022, global searches for the keyword “generative AI” had experienced an increase in the previous six months. The search terms for generative artificial intelligence hit a popularity score of 100 index points during the week starting on February 12, 2023. 

This surge of interest coincided with the introduction of ChatGPT, an AI chatbot model developed by the United States-based research company OpenAI. If you’re curious about the current trends, you can explore search interest for AI on Google Trends.

What is generative AI?

Generative AI, or generative artificial intelligence, refers to AI systems capable of creating new content such as text, images, videos, or other data, often in response to user prompts. These systems use neural networks to learn patterns and structures from large datasets and then generate new data with similar characteristics. 

Examples include AI models like ChatGPT, which generates text, and DALL-E, which creates images from text descriptions. Generative AI has applications across various fields, including art, writing, design, and more, but it also raises concerns about potential misuse, such as creating deepfakes or replacing human jobs.

What is the difference between generative and discriminative AI?

Generative and discriminative models are two types of machine learning algorithms with different objectives and approaches:

Generative Models:

• Aim to model the joint probability distribution of the input features and the output labels.
• Can generate new data instances that are similar to the training data.
• Examples include Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), and Generative Adversarial Networks (GANs).
• Useful for tasks like text generation, image synthesis, and handling incomplete data.

Discriminative Models:

• Focus on modeling the conditional probability of the output label given the input features.
• Aim to find the decision boundary between different classes to make predictions.
• Do not generate new data instances but classify existing ones.
• Examples include Logistic Regression, Support Vector Machines (SVMs), and Decision Trees.

In essence, generative models learn the underlying data distribution to create new examples, while discriminative models learn to distinguish between different classes to make prediction.

Certainly! One well-known example of a generative model is the Generative Adversarial Network (GAN). GANs consist of two neural networks, the generator and the discriminator, which are trained simultaneously through a competitive process:

• The generator creates new data instances that mimic the training data.
• The discriminator evaluates them, trying to distinguish between real and generated data.

The process continues until the generator becomes so good at producing data that the discriminator can’t tell the difference between real and generated instances. This makes GANs powerful for tasks like creating realistic images, synthesizing audio, and more.