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Only Narrow AI actually exists today. General AI and Super AI remain theoretical — important for long-term planning, but not deployed. The full taxonomy along two axes: capabilities (Narrow → General → Super) and functionality (Reactive → Limited Memory → Theory of Mind → Self-Aware), plus Generative and Agentic AI.
From the Turing Test of 1950 to today’s multi-trillion-dollar market — fifteen sections tracing the full arc: the 1956 Dartmouth Conference, expert systems, the AI winters, the 2010s neural network revival, the 2017 transformer breakthrough, and the generative-AI explosion. 75 years of how machines learned to learn.
Zero programming background. Zero math prerequisites. 40–60 hours of self-paced reading across ten chapters — foundations (concepts, math, Python), core AI (machine learning, deep learning, LLMs), and specialisations (computer vision, frameworks, ethics, the future). Built for students, professionals, and curious minds who want to actually understand AI.
A Python agent that watches a GitHub PR, pulls the diff, hands it to a locally running Ollama LLM, and posts a structured code review as a PR comment — bugs, quality, performance, security. No cloud API costs, ~45-second average review time, complete developer walkthrough.
An AI agent is an LLM in a loop — calling tools, reading memory, deciding what to do next, until the task is done. Build one with Claude: the ReAct pattern, Claude Agent SDK, MCP integration, multi-agent orchestration, memory patterns, and 100+ Python and TypeScript examples.
Traditional software follows hardcoded rules. An AI agent observes, reasons, decides, and acts — until a goal is achieved, with no human guidance. The ReAct loop, agent vs assistant vs bot, multi-agent orchestration, the major frameworks (LangChain, AutoGen, Bedrock), and the future of agentic AI.
From McCarthy’s 1956 Dartmouth definition to today’s multi-trillion-dollar industry — what AI actually is, how it learns and reasons, narrow vs general intelligence, real-world applications, the ethics around bias and accountability, and where the field is heading. Synthesised from 11 leading sources including IBM, McKinsey and Stanford.