GenAI Overview

What is generative AI, the AI/ML/DL/GenAI hierarchy, evolution and impact

Generative AI refers to artificial intelligence systems that can create new content — text, images, audio, video, code — that resembles real-world data. Unlike discriminative models that classify inputs, generative models learn the underlying data distribution and sample from it to produce novel outputs.

Generative AI sits at the innermost circle of the AI hierarchy: it is a subset of Deep Learning, which is a subset of Machine Learning, which is a subset of AI.

Key Points

  • Generative AI produces new content rather than just classifying existing content
  • Built on top of foundation models trained on massive datasets with self-supervised learning
  • Key architectures: Transformers (text), Diffusion Models (images), GANs (images/video)
  • Large Language Models (LLMs) are the dominant form of generative AI for text
  • Key capabilities: text generation, code generation, image synthesis, translation, summarisation
  • Emergent capabilities: abilities not explicitly trained for, arising from scale
  • Hallucination: models confidently generating plausible-but-false information
  • Context window: the amount of text a model can "see" and reason about at once
Artificial Intelligence Machine Learning Deep Learning Generative AI Subset of Deep Learning

Generative AI is a subset of Deep Learning. Not all AI is generative — traditional ML models are discriminative.

Real-World Example

ChatGPT reached 100 million users in 2 months — faster than any consumer product in history. It demonstrated that generative AI could hold coherent multi-turn conversations, write code, explain complex topics, and pass professional exams (USMLE, Bar Exam, CPA).

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