Generative AI refers to artificial intelligence models that create new content, including text, images, music, and more. It has revolutionized various industries, from creative arts to software development. This document outlines the progression of generative AI from basic principles to advanced techniques.
Generative models learn patterns in data and generate new content that resembles the training data.
Types of generative models:
Autoregressive Models (e.g., GPT, RNNs, LSTMs)
Variational Autoencoders (VAEs)
Generative Adversarial Networks (GANs)
Early generative AI relied on predefined rules and statistical techniques (e.g., Markov Chains, Hidden Markov Models).
Example: Simple text generators using N-grams.
Text: Word embeddings (Word2Vec, BERT, GPT).
Image: CNNs (ResNet, EfficientNet) for feature extraction.
Audio: Spectrogram analysis using RNNs or transformers.
Introduction to artificial neural networks (ANNs) for pattern recognition.
Feedforward networks and their role in early generative models.
Example: Handwritten digit generation using simple feedforward networks.
Introduction to Recurrent Neural Networks (RNNs) for text generation.
Long Short-Term Memory (LSTMs) to handle long-range dependencies.
Example: Simple chatbot responses using RNNs.
This stage focuses on more sophisticated deep learning techniques and real-world applications.
Introduction to the Transformer architecture (Vaswani et al., 2017).
How GPT (Generative Pre-trained Transformer) models improve text generation.
Example: GPT-based chatbots generating human-like responses.
VAEs generate continuous latent representations for structured output generation.
Applications: Image synthesis, music generation, and style transfer.
Introduction to GANs (Goodfellow et al., 2014) and how they generate high-quality images.
Discriminator vs. Generator concept.
Applications: Deepfake technology, AI-generated art.
Emerging alternative to GANs for high-fidelity image generation.
Example: DALL·E, Stable Diffusion for realistic image synthesis.
Fine-tuning GPT models for domain-specific text generation.
Example: Medical text generation using fine-tuned GPT models.
Understanding AI bias and risks in generative content.
Implementing debiasing techniques in training datasets.
Example: Preventing bias in AI-generated hiring recommendations.
At this level, generative AI leverages cutting-edge architectures and innovations.
Models combining text, images, and audio (e.g., GPT-4, CLIP, Flamingo).
Applications: AI-generated videos, multimodal search engines.
Example: OpenAI’s DALL·E 3 for text-to-image synthesis.
Optimizing user prompts to get high-quality AI-generated content.
Human-in-the-loop AI generation for improved outputs.
Example: Artists using AI to co-create digital paintings.
Training AI without labeled data for more scalable models.
Example: BERT-style pretraining for better content generation.
Using Reinforcement Learning from Human Feedback (RLHF) to improve AI safety and alignment.
Example: ChatGPT’s use of RLHF for more helpful and truthful responses.
AI-Generated Code: Copilot, Code Llama for software development.
AI-Generated 3D Content: Neural Radiance Fields (NeRFs) for 3D scene synthesis.
AI-Generated Music & Video: Deep learning models composing music and creating realistic video.
| Level | Key Techniques | Examples/Models |
|---|---|---|
| Basic | Rule-Based Models, N-Grams, Simple RNNs | Markov Chains, LSTMs |
| Basic Neural Networks, Autoencoders | Handwritten Digit Generation | |
| Intermediate | Transformer Models, GPT | Chatbots, AI Writing Assistants |
| VAEs, GANs, Diffusion Models | AI Art, Deepfake Generation | |
| Fine-Tuning, Bias Mitigation | Domain-Specific Text Generation | |
| Advanced | Large Multimodal Models | GPT-4, CLIP, DALL·E 3 |
| RLHF, Self-Supervised Learning | ChatGPT, BERT-style models | |
| Future AI Trends | AI-Generated 3D, Music, Code |
