AI models flub these intelligence tests. Can you fare any better?
Current frontier models still fail these benchmarks. Here's why it matters for capability eval.
Why it matters
AI capability benchmarking through puzzle and game performance reveals gaps in model reasoning and generalization — a key metric for practitioners evaluating which models to adopt and for enthusiasts tracking the frontier labs' progress toward AGI-adjacent capabilities.
The key facts
9 to knowArticle frames puzzles/games as standard capability tests for AI models
References historical context: 'machine learning' term popularized in 1959 by Arthur Samuel at IBM
Suggests current frontier models have measurable failures on specific benchmark categories
MIT Technology Review publication — credible source on AI benchmarking
AI models underperform on intelligence test puzzles
Puzzles used as capability benchmarking mechanism since early ML era
Story pegs to historical precedent (1959 Arthur Samuel, machine learning term)
Interactive/comparative structure (human vs. model performance)
Published by MIT Technology Review (credible source on frontier capability)
Go to the source
MIT Technology Reviewtechnologyreview.com
Publisher excerpt: Puzzles and games have been central to AI development since the very beginning. Just as we humans like to test our smarts with crosswords or logic puzzles, developers can test how far models have advanced with a gaming gauntlet. The term “machine learning” was popularized in a 1959 article by the…