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.

45 min · Reviewed 2026

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.

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.ARC tasks measure learning a rule from 2-3 examples and applying it to novel inputs.

Competing frameworks

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

If you believe intelligence is skill count, scale wins
If you believe it is generalization, you need new architectures
If it is rationality, alignment becomes the central problem
Your framework shapes which benchmarks you trust

The measure of intelligence is the ability to change.
— Albert Einstein (attributed)

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.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-what-is-intelligence-really

What is the core idea behind "What Is Intelligence, Really? A Working Framework"?
1. 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.
2. 'Show your work' often gets the AI to actually reason.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
Which term best describes a foundational idea in "What Is Intelligence, Really? A Working Framework"?
1. generalization
2. intelligence
3. priors
4. ARC-AGI
A learner studying What Is Intelligence, Really? A Working Framework would need to understand which concept?
1. intelligence
2. priors
3. generalization
4. ARC-AGI
Which of these is directly relevant to What Is Intelligence, Really? A Working Framework?
1. intelligence
2. generalization
3. ARC-AGI
4. priors
Which of the following is a key point about What Is Intelligence, Really? A Working Framework?
1. Skill-acquisition efficiency: how fast you learn new tasks
2. Scope: the variety of tasks the agent can handle
3. Priors: what the agent already knows going in
4. Experience: the data and practice it has had
Which of these does NOT belong in a discussion of What Is Intelligence, Really? A Working Framework?
1. Skill-acquisition efficiency: how fast you learn new tasks
2. 'Show your work' often gets the AI to actually reason.
3. Scope: the variety of tasks the agent can handle
4. Priors: what the agent already knows going in
Which statement is accurate regarding What Is Intelligence, Really? A Working Framework?
1. If you believe it is generalization, you need new architectures
2. If it is rationality, alignment becomes the central problem
3. If you believe intelligence is skill count, scale wins
4. Your framework shapes which benchmarks you trust
Which of these does NOT belong in a discussion of What Is Intelligence, Really? A Working Framework?
1. If you believe it is generalization, you need new architectures
2. If you believe intelligence is skill count, scale wins
3. 'Show your work' often gets the AI to actually reason.
4. If it is rationality, alignment becomes the central problem
What is the key insight about "The key shift" in the context of What Is Intelligence, Really? A Working Framework?
1. Intelligence is not a skill itself. It is the meta-skill of acquiring skills.
2. 'Show your work' often gets the AI to actually reason.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
What is the key insight about "Watch the verb" in the context of What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. Reads, understands, knows, and reasons are doing heavy lifting in most AI claims.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
What is the recommended tip about "Ground your practice in fundamentals" in the context of What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. AI doesn't think — it picks the next word by guessing what fits best.
3. Every AI capability has an underlying mechanism. Understanding that mechanism tells you where it'll fail — which is more…
4. Show why parallelizing attention enabled the scale era.
Which statement accurately describes an aspect of What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. AI doesn't think — it picks the next word by guessing what fits best.
3. Show why parallelizing attention enabled the scale era.
4. You cannot evaluate something you cannot define. Psychologists, philosophers, and computer scientists have all taken cracks at intelligence,…
What does working with What Is Intelligence, Really? A Working Framework typically involve?
1. For our purposes, start with François Chollet's working definition from his 2019 paper: intelligence is a measure of skill-acquisition effic…
2. 'Show your work' often gets the AI to actually reason.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
Which of the following is true about What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. ARC is a set of visual reasoning puzzles designed so each task requires inferring a rule from a handful of examples.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
Which best describes the scope of "What Is Intelligence, Really? A Working Framework"?
1. It is unrelated to foundations workflows
2. It applies only to the opposite beginner tier
3. It focuses on Before we can judge whether an AI is intelligent, we need a framework for what intelligence even mea
4. It was deprecated in 2024 and no longer relevant

← Back to interactive lesson

Tendril · Creators · AI Foundations

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.

45 min · Reviewed 2026

The Definition Problem

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

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.ARC tasks measure learning a rule from 2-3 examples and applying it to novel inputs.

Competing frameworks

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

If you believe intelligence is skill count, scale wins
If you believe it is generalization, you need new architectures
If it is rationality, alignment becomes the central problem
Your framework shapes which benchmarks you trust

The measure of intelligence is the ability to change.
— Albert Einstein (attributed)

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.

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-what-is-intelligence-really

What is the core idea behind "What Is Intelligence, Really? A Working Framework"?
1. 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.
2. 'Show your work' often gets the AI to actually reason.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
Which term best describes a foundational idea in "What Is Intelligence, Really? A Working Framework"?
1. generalization
2. intelligence
3. priors
4. ARC-AGI
A learner studying What Is Intelligence, Really? A Working Framework would need to understand which concept?
1. intelligence
2. priors
3. generalization
4. ARC-AGI
Which of these is directly relevant to What Is Intelligence, Really? A Working Framework?
1. intelligence
2. generalization
3. ARC-AGI
4. priors
Which of the following is a key point about What Is Intelligence, Really? A Working Framework?
1. Skill-acquisition efficiency: how fast you learn new tasks
2. Scope: the variety of tasks the agent can handle
3. Priors: what the agent already knows going in
4. Experience: the data and practice it has had
Which of these does NOT belong in a discussion of What Is Intelligence, Really? A Working Framework?
1. Skill-acquisition efficiency: how fast you learn new tasks
2. 'Show your work' often gets the AI to actually reason.
3. Scope: the variety of tasks the agent can handle
4. Priors: what the agent already knows going in
Which statement is accurate regarding What Is Intelligence, Really? A Working Framework?
1. If you believe it is generalization, you need new architectures
2. If it is rationality, alignment becomes the central problem
3. If you believe intelligence is skill count, scale wins
4. Your framework shapes which benchmarks you trust
Which of these does NOT belong in a discussion of What Is Intelligence, Really? A Working Framework?
1. If you believe it is generalization, you need new architectures
2. If you believe intelligence is skill count, scale wins
3. 'Show your work' often gets the AI to actually reason.
4. If it is rationality, alignment becomes the central problem
What is the key insight about "The key shift" in the context of What Is Intelligence, Really? A Working Framework?
1. Intelligence is not a skill itself. It is the meta-skill of acquiring skills.
2. 'Show your work' often gets the AI to actually reason.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
What is the key insight about "Watch the verb" in the context of What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. Reads, understands, knows, and reasons are doing heavy lifting in most AI claims.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
What is the recommended tip about "Ground your practice in fundamentals" in the context of What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. AI doesn't think — it picks the next word by guessing what fits best.
3. Every AI capability has an underlying mechanism. Understanding that mechanism tells you where it'll fail — which is more…
4. Show why parallelizing attention enabled the scale era.
Which statement accurately describes an aspect of What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. AI doesn't think — it picks the next word by guessing what fits best.
3. Show why parallelizing attention enabled the scale era.
4. You cannot evaluate something you cannot define. Psychologists, philosophers, and computer scientists have all taken cracks at intelligence,…
What does working with What Is Intelligence, Really? A Working Framework typically involve?
1. For our purposes, start with François Chollet's working definition from his 2019 paper: intelligence is a measure of skill-acquisition effic…
2. 'Show your work' often gets the AI to actually reason.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
Which of the following is true about What Is Intelligence, Really? A Working Framework?
1. 'Show your work' often gets the AI to actually reason.
2. ARC is a set of visual reasoning puzzles designed so each task requires inferring a rule from a handful of examples.
3. AI doesn't think — it picks the next word by guessing what fits best.
4. Show why parallelizing attention enabled the scale era.
Which best describes the scope of "What Is Intelligence, Really? A Working Framework"?
1. It is unrelated to foundations workflows
2. It applies only to the opposite beginner tier
3. It focuses on Before we can judge whether an AI is intelligent, we need a framework for what intelligence even mea
4. It was deprecated in 2024 and no longer relevant

← Back to interactive lesson