1 Old questions, new machines

Artificial intelligence has long achieved impressive results in narrowly defined tasks, learning to recognize patterns in data, optimize complex decisions, and even defeat expert human players in strategic games. These achievements demonstrated capability, but they remained confined to specific domains, far from anything resembling general understanding. That boundary has begun to shift with the emergence of large language models, as machines can now generate and interpret text at a level that often resembles human communication.

1.1 The question of intelligence returns

1.1.1 Intelligence at the crossroads

1.1.2 When machines began to speak

1.1.3 The illusion of understanding

1.1.4 Why this question matters

1.2 Is it really about the machines?

1.2.1 A question older than bytes

1.2.2 Believing before building

1.2.3 From dreams to designs

1.3 The first circuits of thought

1.3.1 The recipe for modern machines

1.3.2 The architecture of generality

1.3.3 From computation to cognition

1.3.4 The principle of imitation

1.3.5 Learning, surprise, and unpredictability

1.3.6 Turing’s test and its evolving meaning

1.4 Learning to adapt

1.4.1 When rules failed

1.4.2 Living with uncertainty

1.4.3 The spark of scale

1.4.4 The price of complexity

1.4.5 Progress in cycles

1.5 The measure of thought

1.5.1 What we mistake for intelligence

1.5.2 Mind in the world

1.5.3 The return of experience

1.5.4 Inside the imitation

1.5.5 New walls for the room

1.5.6 Syntax, semantics, and the limits of imitation

1.5.7 Complex boundaries

1.5.8 Builders and philosophers

1.6 The shifting meaning of intelligence

1.6.1 Create to redefine

1.6.2 The ever-receding horizon