A riddle about a car wash tests common sense, not AI knowledge

The answer "on foot" looks obvious — until you remember why anyone goes to a car wash in the first place. If a person means to wash their car, they need to get there together with the car, so the logical answer is by car.

That is exactly where the task for AI hides: the 50 metres pull attention onto themselves while the purpose of the trip stays out of frame. A model can reason confidently about the walk and the convenience yet miss the everyday implication of the word "car wash" itself.

This is not an arithmetic check or a question about some rare fact. An example like this tests whether a system can fill in context and tell a literal reading from a person's intent. That is why a short riddle proves more useful than a long prompt full of obvious hints: it shows where a model starts answering smoothly but inappropriately.

There is an important limitation too: this material does not let you compare particular models, work out how often they err, or conclude which system is better. All that is known is that neural networks from major companies have fallen into this trap, and that the question is used as a sort of common-sense benchmark.

When you check an AI's answer, which matters more to you: the literal logic of the phrase, or the model's ability to work out why the person is driving to a car wash at all? Source: All articles / Artificial Intelligence / Habr