Intro to Generative AI
Traditional machine learning relies on "predictive" models: observing data and finding a pattern (e.g., predicting house prices, deciding if an email is spam). Generative AI flips this completely.
What is Generative AI?
Generative AI refers to algorithms capable of generating entirely novel, high-quality content—including text, images, audio, and synthetic data—that resembles the data it was trained on.
The Shift in Capability
Instead of identifying that a photo contains a dog (Discriminative AI), a Generative AI can be asked to "draw a photorealistic photo of a dog wearing a spacesuit on Mars" and synthesize that pixel by pixel.
Core Generative Architectures
1. Transformer Models
The driving force behind modern text generation (like ChatGPT and Google Gemini). Transformers excel at understanding sequential data (like languages) by using "attention mechanisms" to weigh the importance of every word in a sentence simultaneously.
2. Diffusion Models
Used heavily in image generation software like Midjourney, DALL-E, and Stable Diffusion. Diffusion models work by taking pure visual "noise" (like TV static) and slowly refining it step-by-step until a clear image emerges that perfectly matches a text prompt.
3. Generative Adversarial Networks (GANs)
A system containing two neural networks fighting against each other:
- The Generator: Tries to create fake data (like a fake photograph of a human face).
- The Discriminator: Tries to detect if the photograph is real or fake. By competing, the Generator becomes incredibly adept at creating hyper-realistic synthetic media (often used in creating deepfakes).