What is AGI

To make deliberate progress towards more intelligent and human-like systems, we need to be following an appropriate feedback signal. We need to define and evaluate intelligence.

The consensus definition of AGI, "a system that can automate the majority of economically valuable work," while a useful goal, is an incorrect measure of intelligence.

Measuring task-specific skill is not a good proxy for intelligence.

Skill is heavily influenced by prior knowledge and experience. Unlimited priors or unlimited training data allows developers to "buy" levels of skill for a system. This masks a system's own generalization power.

Intelligence lies in broad or general-purpose abilities; it is marked by skill-acquisition and generalization, rather than skill itself.

Here's a better definition for AGI:

AGI is a system that can efficiently acquire new skills outside of its training data.

More formally:

The intelligence of a system is a measure of its skill-acquisition efficiency over a scope of tasks, with respect to priors, experience, and generalization difficulty. François Chollet, "On the Measure of Intelligence"

This means that a system is able to adapt to new problems it has not seen before and that its creators (developers) did not anticipate.

ARC-AGI is the only AI benchmark that measures our progress towards general intelligence.

Hear François explain intelligence. (Lex Friedman interview on YouTube)