Lesson 1 of 2116
What Is Intelligence, Really? A Working Framework
Before we can judge whether an AI is intelligent, we need a framework for what intelligence even means. Draw on Chollet, Dennett, and modern evals.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The Definition Problem
- 2intelligence
- 3generalization
- 4ARC
Concept cluster
Terms to connect while reading
Section 1
The Definition Problem
You cannot evaluate something you cannot define. Psychologists, philosophers, and computer scientists have all taken cracks at intelligence, and none of their answers fully agree. That ambiguity quietly infects every AI debate.
For our purposes, start with François Chollet's working definition from his 2019 paper: intelligence is a measure of skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty. That sounds dense, but it pays off.
Unpacking Chollet's definition
- Skill-acquisition efficiency: how fast you learn new tasks
- Scope: the variety of tasks the agent can handle
- Priors: what the agent already knows going in
- Experience: the data and practice it has had
- Generalization difficulty: how far the new task is from anything seen before
ARC-AGI: a benchmark built on this idea
ARC is a set of visual reasoning puzzles designed so each task requires inferring a rule from a handful of examples. Humans solve most of them. For years, the best AI systems could not pass 30 percent. Reasoning models have closed the gap but still struggle compared to humans.
ARC tasks measure learning a rule from 2-3 examples and applying it to novel inputs.
Input grid → Output grid
. . X . . . X .
. . . . → . . X .
. . . . . . X .
Infer: fill a vertical line below the X.
Apply to a new grid the model has never seen.Competing frameworks
Compare the options
| Framework | Core claim |
|---|---|
| Turing (1950) | If you cannot tell it from a human, call it intelligent |
| Russell & Norvig | Intelligence is rational action maximizing expected utility |
| Chollet | Intelligence is efficient generalization to novel tasks |
| Dennett | Intelligence is intentional stance at scale |
| LeCun's world models | Intelligence requires internal causal simulation |
Why it matters for product decisions
- 1If you believe intelligence is skill count, scale wins
- 2If you believe it is generalization, you need new architectures
- 3If it is rationality, alignment becomes the central problem
- 4Your framework shapes which benchmarks you trust
“The measure of intelligence is the ability to change.”
Key terms in this lesson
The big idea: intelligence is not a single dial. Choose a working definition, then judge every AI claim against that lens. You will cut through most of the noise.
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