Chapter 5 – Machine learning

1.1  What Is Artificial Intelligence?

1.1.1  How Much Like a Human: Strong vs. Weak AI

1.1.2  Top-down or Bottom-up: Symbolic vs. Sub-symbolic

1.1.3  A Working Definition

1.1.4  Human Intelligence

1.1.5  Bottom-up and Top-down

1.2  Humans at the Heart

1.3  A Short History of Artificial Intelligence

1.3.1  The Development of AI

1.3.2  The Physical Symbol System Hypothesis

1.3.3  Sub-symbolic Spring

1.3.4  AI Renaissance

1.3.5  Moving Onwards

1.4  Structure of This Book -- A Landscape of AI

adversarial learning, AI winter, alien intelligence, AlphaGo, Alvey Programme, Analytical Engine, artificial intelligence, artificial neural networks, autonomous vehicles, Babbage, Charles, Berners-Lee, Tim, bias, big data, Bombe, chatbot, Clarke, Arthur C., computer chess, computer vision, connectionist model, constraint satisfaction, Dartmouth Workshop, deep neural network, Descartes, Réne, Difference Engine, ELIZA, Enigma machine, expert system, explainable AI, facial recognition, Fifth Generation Computer Project, Frankenstein, game playing, general problem solving, Go, Google, GPT-4, HAL 9000, heuristic evaluation function, history of AI, home automation, human insight, human intelligence, IBM Watson, Jeopardy!, knowledge representation, knowledge-rich AI, large language model, Lee Sedol, logic programming, logical reasoning, Lovelace, Ada, machine learning, natural language understanding, neural network, Newell, Alan, Ovid, PageRank, pattern matching, physical symbol system hypothesis, pragmatic approach to AI, Pygmalion, reservoir computing, robotics, search engine, semantic web, Shelley, Mary, SHRDLU, Simon, Herbert, speech recognition, strong AI, sub-symbolic spring, sub-symbolic systems, symbolic systems, System 1, System 2, Turing test, Turing, Alan, weak AI, Weizenbaum, Joseph, Winograd, Terry