Science-Communication

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.

I am on the spectrum. Code is easy; emotions are not. This post is about the phrase ‘making mental illness visible’, what science actually tells us about that goal, why a non-affected person fundamentally cannot understand — and why trying still matters.

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.

In September 2020 I gave a teacher training talk on stellar formation and the misconceptions students bring into class. The misconception list was long enough to be its own document. Here it is, with commentary. Includes: the Sun as a heat-planet, gravity that only works when things move, metals that always existed, and the obligatory complaint about quantum leaps.