agi-framework-chollet

Evaluate AI progress by applying Chollet's framework to distinguish memorized skills from fluid intelligence. This framework provides critical assessment criteria to determine if a system demonstrates true generalization rather than rote task performance.

Minsky-style intelligence is task-based, focusing on specific capabilities, whereas McCarthy-style intelligence is adaptation-based, focusing on generalization potential. Comparing these definitions helps evaluate whether an AI system possesses true fluid intelligence.

Use the Abstraction and Reasoning Corpus (ARC) benchmarks to separate memory-based performance from actual reasoning. ARC evaluates test-time adaptation and program synthesis capabilities to expose whether a model relies on rote memorization.

Use test-time adaptation to analyze AI architectures when evaluating Type 1 versus Type 2 abstraction concepts. This approach assesses whether a model can dynamically solve novel problems instead of relying solely on memorized skills.

Yes, you can use this framework to critically evaluate AGI claims by mapping reported capabilities to test-time adaptation and abstraction questions. This process reveals whether a system exhibits flexible problem solving or simply memorized task performance.