Nlp

A new video went viral last week: same question, “should I drive to the car wash?”, different wrong answer — the AI said to walk instead. This is neither the tokenisation failure from the strawberry post nor the grounding failure from the rainy-day post. It is a pragmatic inference failure: the model understood all the words and (probably) had the right world state, but assigned its response to the wrong interpretation of the question. A third and more subtle failure mode, with Grice as the theoretical handle.

A viral video this month showed an AI assistant confidently answering “should I go to the car wash today?” without knowing it was raining outside. The internet found it funny. The failure mode is real but distinct from the strawberry counting problem — this is not a representation issue, it is a grounding issue. The model understood the question perfectly. What it lacked was access to the state of the world the question was about.

On 2 December 2024 I gave three workshops at HfMT Köln’s Thementag on AI and music education. The handouts covered data protection, AI tools for students, and AI in teaching. This post is the argument behind them — focused on the curriculum question that none of the tools answer on their own: what should change, and what should not?

In September 2024, OpenAI revealed that its new o1 model had been code-named “Strawberry” internally — the same word that language models have famously been unable to count letters in. The irony was too perfect to pass up. But the counting failure is not a sign that LLMs are naive or broken. It is a precise, informative symptom of how they process text. Here is the actual explanation, with a minimum of hand-waving.