Introduction to generative artificial intelligence - what it is, how it works, and why it matters.
Generative AI is a class of artificial intelligence systems that can create new content β text, images, audio, video, and code β rather than simply analyzing or classifying existing data. Unlike traditional machine learning models that predict labels or numbers, generative models learn the underlying patterns and distribution of their training data, then produce entirely new outputs that follow those same patterns.
The field exploded into mainstream awareness with the release of ChatGPT in November 2022, but the foundations were laid years earlier with the Transformer architecture (2017), GPT-1 (2018), and the steady scaling of models that revealed emergent abilities at larger sizes. Today, generative AI powers everything from coding assistants to image generators, and understanding how it works is the essential starting point for anyone working with modern AI tools.
Generative AI operates across multiple modalities. Large Language Models (LLMs) like GPT-4 and Claude generate text. Diffusion models like Stable Diffusion and DALL-E create images. Models like Sora generate video, while Whisper and similar systems handle audio. Increasingly, multimodal models combine several of these capabilities in a single system.
Generative vs Traditional AI/ML
Traditional AI classifies or predicts from data. Generative AI creates new content β text, images, code β by learning patterns from training data and producing novel outputs.
Key Generative Modalities
Text (LLMs like GPT, Claude), images (diffusion models like DALL-E, Midjourney), audio (Whisper, TTS), video (Sora, Runway), and code (Codex, StarCoder). Each modality uses different architectures.
History of Generative AI
GPT-1 (2018, 117M params) β GPT-2 (2019, 1.5B) β GPT-3 (2020, 175B) β ChatGPT (Nov 2022, public launch) β GPT-4 (2023, multimodal). Each step brought qualitative leaps in capability.
Generative vs Discriminative Models
Discriminative models learn decision boundaries (cat vs dog). Generative models learn the full data distribution and can sample new examples from it. LLMs are generative β they model the probability of text.
The Transformer Architecture
The 2017 "Attention Is All You Need" paper introduced self-attention, enabling parallel processing of sequences. Virtually all modern generative AI β text, image, video β builds on this architecture.
Pre-training at Scale
Models are trained on trillions of tokens from the internet β books, websites, code repos, scientific papers. This phase costs millions of dollars and produces a "foundation model" with general capabilities.
Emergent Abilities
At certain scales, models suddenly gain capabilities not present in smaller versions: in-context learning, chain-of-thought reasoning, code generation. These emerge from scale, not explicit programming.
Real-World Applications
Software development (AI pair programming, code review), content creation (writing, design), healthcare (drug discovery), education (tutoring), finance (analysis), legal (document review).
Open vs Closed Ecosystem
Closed models (GPT-4, Claude) offer superior performance via API. Open-weight models (Llama, Qwen, Mistral) can be run locally, fine-tuned, and inspected. Both ecosystems are thriving.
Current Limitations
Hallucinations (confident but wrong outputs), reasoning gaps (failing on novel logic), context constraints (limited working memory), lack of real-time knowledge, and inability to truly "understand."
Generative AIAI systems that create new content (text, images, code) rather than just analyzing existing data.
Foundation ModelA large model pre-trained on broad data that serves as a base for many downstream tasks.
TransformerThe neural network architecture (2017) that powers virtually all modern generative AI.
Pre-trainingThe initial phase of training a model on massive datasets before task-specific adaptation.
Emergent AbilitiesCapabilities that appear suddenly in models only when they reach a certain scale.